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Human-environment interaction has significantly altered the pedosphere since the Neolithic, if not since the early Holocene. In the course of clearance, agriculture, and (wood) pasture soils have been deeply modified or eroded. These types of land use practices but above all forms of sedentariness spread alongside floodplains and trajectories were oriented towards loess covered areas where fertile soils could develop. Besides this, also peripheral / marginal regions were settled due to population pressure or other factors. Evidence for landscape history and development can be found within archeological sites but also overbank deposits and anthropogenic slope deposits document vast transformation processes.
The presented investigations took place within the natural region of the Windsheimer Bucht which is locat-ed in the district of Middle Franconia in northern Bavaria, Germany. In this area, Holocene soils predomi-nantly developed within mudstones of the Middle to Upper Triassic. The soil texture is extremely clay-rich which renders the soils problematic with regard to cultivation management. As a peculiarity, the gypsum underlying the mudstones is prone to karstification processes and resulting proceeding geomorphological processes shape the surface of the landscape. In the course of gypsum mining the karst forms are being exposed and archeological findings are being documented. The latter mainly date back to a span from the Neolithic to the Iron Age, but partly are of Younger Paleolithic origin. Especially subsidence sinkholes are capable of storing pedosediments of several meters in thickness. Despite the high clay content and connect-ed pedoturbation processes, the excavated sequences are stratigraphically and pedologically well-differentiated. The archives occur in the context of settlement structures such as pits and postholes; there-fore, they developed at the interface of natural developments and human impact on their surroundings.
The main original research questions that were formulated within the general frame of a project funded by the Deutsche Forschungsgemeinschaft (DFG-projects Te295/15-1 and -2 and Fa390/9-1 and -2) focused on the attractors of the peripheral region for early settlers, the pedological conditions before land use, but also the impact of humans on soils and karst dynamics through time. In the course of the in hand study, the pedosedimentary archives have been approached with a multimethodological toolset which consisted of field analyses, soil morphological analyses from micro- to macro-scale, spectrophotometric (color), (laser) granulometric, and (iron-) pedochemical analyses. The numerical chronological frame was spanned by radiocarbon dating of different organic remains and bulk material if soil organic carbon was supposed-ly high. The result is a multi-dimensional data set that consists of analyses on different spatial scales but also on different levels of measurement. Thus, qualitative, semi-quantitative, and quantitative data consti-tute the basis for discussion. While the grain-size analyses underline the general sedimentological differen-tiation of the records and further affirm the high clay content within the pedosedimentary layers, iron-pedochemical analyses indicate an interplay between oxidation of iron and its chemical reduction. This is also manifested within the spectrophotometric record. Especially the versatile pedogenic characteristics that have been identified by field analyses are confirmed within the thin sections and, by considering all different analyses, the polygenic character of the pedosediments is emphasized.
After stressing the general pedological specificities among the different investigated sites within the re-search area, for the collected data, the research further branches into the subjects of general notions on pedogenesis in clayey material and the classification of the respective pedosediments according to paleo-pedological concepts but also recent schemes. Concerning the latter, it becomes evident that established principles cannot be applied to the studied pedosediments without major adaptions. This underlines the specific characteristics of the material.
The basis for further interpretations is the evaluation of the multi-level data set for the single records with regard to profile development and pedogenic processes. Hereby, the main drivers of pedogenesis could be identified, which are karst dynamics, land use, and subtle changes in parent material due to the admixture of slope deposits that contain allochthonous eolian material. The latter underlines the importance of Pleis-tocene preconditioning for understanding Holocene landscape dynamics. At the same time, a differentia-tion between the mentioned factors and Holocene climate development is difficult. The following compila-tion of record and localities within the given time frame unveils synchronous as well as asynchronous de-velopments; however, a clear connection between phases of Holocene climate and pedogenesis within the pedosediments cannot be established. Instead, it becomes evident that site specific factors or those that act on the scale of the micro-catchment of the investigated records are decisive.
The aforementioned main topics of the project are also considered in the in hand study from a soil-geographic perspective: it is possible that before land use, there was an insular or thin cover by loess sedi-ments or at least upper layers (according to the concept of periglacial cover beds) which constituted the parent material for Holocene soil formation. The according soils, which were superior for agricultural purposes compared to those developed on the autochthonous mudstones, were eroded which exposed the clayey Upper to Middle Triassic beds. Erosion was aggravated due to the impermeable mudstones which enhanced overland flow and interflow within the overlying silty (loessic) material. This is further support-ed by the notions on erodibility of the clayey material that are derived from the comparison of conven-tional and laser granulometric analyses: probably, the clayey pedosediments are capable of forming micro-aggregates that can easily be eroded during heavy rainfall events despite the general consent that material with heavy texture should be rather resistant.
The study presents a comprehensive view on clay-rich pedosediments and the complex effects of human-environment interaction on pedogenic as well as sedimentary processes through time that have not been investigated in such detail before. In this context, the multi-level soil morphological analyses and their necessity for a genetic interpretation with regard to the influence of natural versus anthropogenic factors need to be emphasized. Based on quantitative laboratory analytical data only, a respective differentiation would not be possible. This underlines the importance of the chosen soil-geographic multi-methodological approach for answering questions with regard to human-environment interaction but also geoarcheology in general.
Permafrost degradation is observed all over the world as a consequence of climate change and the associated Arctic amplification, which has severe implications for the environment. Landslides, increased rates of surface deformation, rising likelihood of infrastructure damage, amplified coastal erosion rates, and the potential turnover of permafrost from a carbon sink to a carbon source are thereby exemplary implications linked to the thawing of frozen ground material. In this context, satellite earth observation is a potent tool for the identification and continuous monitoring of relevant processes and features on a cheap, long-term, spatially explicit, and operational basis as well as up to a circumpolar scale.
A total of 325 articles published in 30 different international journals during the past two decades were investigated on the basis of studied environmental foci, remote sensing platforms, sensor combinations, applied spatio-temporal resolutions, and study locations in an extensive review on past achievements, current trends, as well as future potentials and challenges of satellite earth observation for permafrost related analyses. The development of analysed environmental subjects, utilized sensors and platforms, and the number of annually published articles over time are addressed in detail. Studies linked to atmospheric features and processes, such as the release of greenhouse gas emissions, appear to be strongly under-represented. Investigations on the spatial distribution of study locations revealed distinct study clusters across the Arctic. At the same time, large sections of the continuous permafrost domain are only poorly covered and remain to be investigated in detail. A general trend towards increasing attention in satellite earth observation of permafrost and related processes and features was observed. The overall amount of published articles hereby more than doubled since the year 2015. New sources of satellite data, such as the Sentinel satellites and the Methane Remote Sensing LiDAR Mission (Merlin), as well as novel methodological approaches, such as data fusion and deep learning, will thereby likely improve our understanding of the thermal state and distribution of permafrost, and the effects of its degradation. Furthermore, cloud-based big data processing platforms (e.g. Google Earth Engine (GEE)) will further enable sophisticated and long-term analyses on increasingly larger scales and at high spatial resolutions.
In this thesis, a specific focus was put on Arctic permafrost coasts, which feature increasing vulnerability to environmental parameters, such as the thawing of frozen ground, and are therefore associated with amplified erosion rates. In particular, a novel monitoring framework for quantifying Arctic coastal erosion rates within the permafrost domain at high spatial resolution and on a circum-Arctic scale is presented within this thesis. Challenging illumination conditions and frequent cloud cover restrict the applicability of optical satellite imagery in Arctic regions. In order to overcome these limitations, Synthetic Aperture RADAR (SAR) data derived from Sentinel-1 (S1), which is largely independent from sun illumination and weather conditions, was utilized. Annual SAR composites covering the months June–September were combined with a Deep Learning (DL) framework and a Change Vector Analysis (CVA) approach to generate both a high-quality and circum-Arctic coastline product as well as a coastal change product that highlights areas of erosion and build-up. Annual composites in the form of standard deviation (sd) and median backscatter were computed and used as inputs for both the DL framework and the CVA coastal change quantification. The final DL-based coastline product covered a total of 161,600 km of Arctic coastline and featured a median accuracy of ±6.3 m to the manually digitized reference data. Annual coastal change quantification between 2017–2021 indicated erosion rates of up to 67 m per year for some areas based on 400 m coastal segments. In total, 12.24% of the investigated coastline featured an average erosion rate of 3.8 m per year, which corresponds to 17.83 km2 of annually eroded land area. Multiple quality layers associated to both products, the generated DL-coastline and the coastal change rates, are provided on a pixel basis to further assess the accuracy and applicability of the proposed data, methods, and products.
Lastly, the extracted circum-Arctic erosion rates were utilized as a basis in an experimental framework for estimating the amount of permafrost and carbon loss as a result of eroding permafrost coastlines. Information on permafrost fraction, Active Layer Thickness (ALT), soil carbon content, and surface elevation were thereby combined with the aforementioned erosion rates. While the proposed experimental framework provides a valuable outline for quantifying the volume loss of frozen ground and carbon release, extensive validation of the utilized environmental products and resulting volume loss numbers based on 200 m segments are necessary. Furthermore, data of higher spatial resolution and information of carbon content for deeper soil depths are required for more accurate estimates.
Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.
Accurate crop monitoring in response to climate change at a regional or field scale
plays a significant role in developing agricultural policies, improving food security,
forecasting, and analysing global trade trends. Climate change is expected to
significantly impact agriculture, with shifts in temperature, precipitation patterns, and
extreme weather events negatively affecting crop yields, soil fertility, water availability,
biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable
information combined with crop growth models (CGMs) for yield assessment by
monitoring crop development, detecting crop changes, and assessing the impact of
climate change on crop yields. This dissertation aims to investigate the potential of RS
data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR)
for the Free State of Bavaria (70,550 km2
), Germany. The first chapter of the dissertation
describes the reasons favouring the importance of accurate crop yield predictions for
achieving sustainability in agriculture. Chapter second explores the accuracy
assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data
(high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low
spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one
day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial
and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud
or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more
suitable for agricultural monitoring than the other synthetic products fused. Chapter
third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter
2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The
chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal
(8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light
use efficiency approach (LUE)) for accurate crop yield estimations of both crop types.
Chapter third observes that the observations of high temporal resolution (8-day)
products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately
measuring the yield of WW and OSR. The chapter investigates that the simple light use
efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more
precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher
input parameters. Chapter four researches the relationship of spatiotemporal fusion
modelling using STRAFM on crop yield prediction for WW and OSR using the LUE
model for Bavaria from 2001 to 2019. The chapter states the high positive correlation
coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and
modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter
analyses the impact of climate variables on crop yield predictions by observing an
increase in R2
(0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when
the climate effect is included in the model. The fifth chapter suggests that the coupling
of the LUE model to the random forest (RF) model can further reduce the relative root
mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by
14.3% (for both WW and OSR), compared to results just relying on LUE. The same
chapter concludes that satellite-based crop biomass, solar radiation, and temperature
are the most influential variables in the yield prediction of both crop types. Chapter six
attempts to discuss both pros and cons of RS technology while analysing the impact of
land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that
the modelled biomass of both crops is positively impacted by land use diversity to the
radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The
chapter also discusses the future implications by stating that including some dependent
factors (such as the management practices used, soil health, pest management, and
pollinators) could improve the relationship of RS-modelled crop yields with
biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as
unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving
accurate crop yield predictions for precision farming. In addition, the chapter highlights
the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher
crop yield accuracies.
Grasslands shape many landscapes of the earth as they cover about one-third of its surface. They are home and provide livelihood for billions of people and are mainly used as source of forage for animals. However, grasslands fulfill many additional ecosystem functions next to fodder production, such as storage of carbon, water filtration, provision of habitats and cultural values. They play a role in climate change (mitigation) and in preserving biodiversity and ecosystem functions on a global scale. The degree to what these ecosystem functions are present within grassland ecosystems is largely determined by the management. Individual management practices and the use intensity influence the species composition as well as functions, like carbon storage, while higher use intensities (e.g. high mowing frequencies) usually show a negative impact. Especially in Central European countries, like in Germany, the determining influence of grassland management on its physiognomy and ecosystem functions leads to a large variability and small-scale alternations of grassland parcels. Large-scale information on the management and use intensity of grasslands is not available. Consequently, estimations of grassland ecosystem functions are challenging which, however, would be required for large-scale assessments of the status of grassland ecosystems and optimized management plans for the future. The topic of this thesis tackles this gap by investigating the major grassland management practice in Germany, which is mowing, for multiple years, in high spatial resolution
and on a national scale.
Earth Observation (EO) has the advantage of providing information of the earth’s surface on multi-temporal time steps. An extensive literature review on the use of EO for grassland management and production analyses, which was part of this thesis, showed that in particular research on grasslands consisting of small parcels with a large variety of management and use intensity, like common in Central Europe, is underrepresented. Especially
the launch of the Sentinel satellites in the recent past now enables the analyses of such grasslands due to their high spatial and temporal resolution. The literature review specifically on the investigation of grassland mowing events revealed that most previous studies focused on small study areas, were exploratory, only used one sensor type and/or lacked a reference data set with a complete range of management options.
Within this thesis a novel framework to detect grassland mowing events over large areas is presented which was applied and validated for the entire area of Germany for multiple years (2018–2021). The potential of both sensor types, optical (Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel-1) was investigated regarding grassland mowing event detection. Eight EO parameters were investigated, namely the Enhanced Vegetation Index (EVI), the backscatter intensity and the interferometric (InSAR) temporal coherence for both available polarization modes (VV and VH), and the polarimetric (PolSAR) decomposition parameters Entropy, K0 and K1. An extensive reference data set was generated based on daily images of webcams distributed in Germany which resulted in mowing information
for grasslands with the entire possible range of mowing frequencies – from one to six in Germany – and in 1475 reference mowing events for the four years of interest.
For the first time a observation-driven mowing detection approach including data from Sentinel-2 and Sentinel-1 and combining the two was developed, applied and validated on large scale. Based on a subset of the reference data (13 grassland parcels with 44 mowing events) from 2019 the EO parameters were investigated and the detection algorithm
developed and parameterized. This analysis showed that a threshold-based change detection approach based on EVI captured grassland mowing events best, which only failed during periods of clouds. All SAR-based parameters showed a less consistent behavior to mowing events, with PolSAR Entropy and InSAR Coherence VH, however, revealing the
highest potential among them. A second, combined approach based on EVI and a SARbased parameter was developed and tested for PolSAR Entropy and InSAR VH. To avoid additional false positive detections during periods in which mowing events are anyhow reliably detected using optical data, the SAR-based mowing detection was only initiated
during long gaps within the optical time series (< 25 days). Application and validation of
these approaches in a focus region revealed that only using EVI leads to the highest accuracies (F1-Score = 0.65) as combining this approach with SAR-based detection led to a strong increase in falsely detected mowing events resulting in a decrease of accuracies (EVI + PolSAR ENT F1-Score = 0.61; EVI + InSAR COH F1-Score = 0.61).
The mowing detection algorithm based on EVI was applied for the entire area of Germany for the years 2018-2021. It was revealed that the largest share of grasslands with high mowing frequencies (at least four mowing events) can be found in southern/south-eastern Germany. Extensively used grassland (mown up to two times) is distributed within the entire country with larger shares in the center and north-eastern parts of Germany. These patterns stay constant in general, but small fluctuations between the years are visible. Early mown grasslands can be found in southern/south-eastern Germany – in line with high mowing frequency areas – but also in central-western parts. The years 2019 and 2020 revealed higher accuracies based on the 1475 mowing events of the multi-annual validation data set
(F1-Scores of 0.64 and 0.63), 2018 and 2021 lower ones (F1-Score of 0.52 and 0.50).
Based on this new, unprecedented data set, potential influencing factors on the mowing dynamics were investigated. Therefore, climate, topography, soil data and information on conservation schemes were related to mowing dynamics for the year 2020, which showed a high number of valid observations and detection accuracy. It was revealed that there are no strong linear relationships between the mowing frequency or the timing of the first mowing event and the investigated variables. However, it was found that for intensive grassland usage certain climatic and topographic conditions have to be fulfilled, while extensive grasslands appear on the entire spectrum of these variables. Further, higher mowing frequencies occur on soils with influence of ground water and lower mowing frequencies in protected areas. These results show the complex interplay between grassland mowing dynamics and external influences and highlight the challenges of policies aiming to protect grassland ecosystem functions and their need to be adapted to regional circumstances.
Regional climate models (RCMs) are tools used to project future climate change at a regional scale. Despite their high horizontal resolution, RCMs are characterized by systematic biases relative to observations, which can result in unrealistic interpretations of future climate change signals. On the other hand, bias correction (BC) is a popular statistical post-processing technique applied to improve the usability of output from climate models. Like every other statistical technique, BC has its strengths and weaknesses. Hence, within the regional context of Germany, and for temperature and precipitation, this study is dedicated to the assessment of the impact of different BC techniques on the RCM output. The focuses are on the impact of BC on the RCM’s statistical characterization, and physical consistency defined as the spatiotemporal consistency between the bias-corrected variable and the simulated physical mechanisms governing the variable, as well as the correlations between the bias-corrected variable and other (simulated) climate variables. Five BC techniques were applied in adjusting the systematic biases in temperature and precipitation RCM outputs. The BC techniques are linear scaling, empirical quantile mapping, univariate quantile delta mapping, multivariate quantile delta mapping that considers inter-site dependencies, and multivariate quantile delta mapping that considers inter-variable dependencies (MBCn). The results show that each BC technique adds value in reducing the biases in the statistics of the RCM output, though the added value depends on several factors such as the temporal resolution of the data, choice of RCM, climate variable, region, and the metric used in evaluating the BC technique. Further, the raw RCMs reproduced portions of the observed modes of atmospheric circulation in Western Europe, and the observed temperature, and precipitation meteorological patterns in Germany. After the BC, generally, the spatiotemporal configurations of the simulated meteorological patterns as well as the governing large-scale mechanisms were reproduced.
However, at a more localized spatial scale for the individual meteorological patterns, the BC changed the simulated co-variability of some grids, especially for precipitation. Concerning the co-variability among the variables, a physically interpretable positive correlation was found between temperature and precipitation during boreal winter in both models and observations. For most grid boxes in the study domain and on average, the BC techniques that do not adjust inter-variable dependency did not notably change the simulated correlations between the climate variables. However, depending on the grid box, the (univariate) BC techniques tend to degrade the simulated temporal correlations between temperature and precipitation. Further, MBCn which adjusts biases in inter-variable dependency has the skill to improve the correlations between the simulated variables towards observations.
The Seville Strategy spurred a signifi cant paradigm shift in UNESCO’s MAB Programme, re-conceptualising the research programme as a modern tool for the dual mandate of nature conservation and sustainable development. However, many biosphere reserves failed to comply with the new regulations and in 2013 the ‘Exit Strategy’ was announced to improve the quality of the global network.
This study presents a global assessment of the implementation of the quality enhancement strategies, highlighting signifi cant differences worldwide through 20 country-specifi c case studies. It concludes that the strategies have been fundamental in improving the credibility and coherence of the MAB Programme. Challenges in the implementation were not unique to individual countries but were common to all Member States with pre-Seville sites, and in many states the process has led to a rejuvenation of national biosphere reserve networks.
The expansion of renewable energies is being driven by the gradual phaseout of fossil fuels in order to reduce greenhouse gas emissions, the steadily increasing demand for energy and, more recently, by geopolitical events. The offshore wind energy sector is on the verge of a massive expansion in Europe, the United Kingdom, China, but also in the USA, South Korea and Vietnam. Accordingly, the largest marine infrastructure projects to date will be carried out in the upcoming decades, with thousands of offshore wind turbines being installed. In order to accompany this process globally and to provide a database for research, development and monitoring, this dissertation presents a deep learning-based approach for object detection that enables the derivation of spatiotemporal developments of offshore wind energy infrastructures from satellite-based radar data of the Sentinel-1 mission.
For training the deep learning models for offshore wind energy infrastructure detection, an approach is presented that makes it possible to synthetically generate remote sensing data and the necessary annotation for the supervised deep learning process. In this synthetic data generation process, expert knowledge about image content and sensor acquisition techniques is made machine-readable. Finally, extensive and highly variable training data sets are generated from this knowledge representation, with which deep learning models can learn to detect objects in real-world satellite data.
The method for the synthetic generation of training data based on expert knowledge offers great potential for deep learning in Earth observation. Applications of deep learning based methods can be developed and tested faster with this procedure. Furthermore, the synthetically generated and thus controllable training data offer the possibility to interpret the learning process of the optimised deep learning models.
The method developed in this dissertation to create synthetic remote sensing training data was finally used to optimise deep learning models for the global detection of offshore wind energy infrastructure. For this purpose, images of the entire global coastline from ESA's Sentinel-1 radar mission were evaluated. The derived data set includes over 9,941 objects, which distinguish offshore wind turbines, transformer stations and offshore wind energy infrastructures under construction from each other. In addition to this spatial detection, a quarterly time series from July 2016 to June 2021 was derived for all objects. This time series reveals the start of construction, the construction phase and the time of completion with subsequent operation for each object.
The derived offshore wind energy infrastructure data set provides the basis for an analysis of the development of the offshore wind energy sector from July 2016 to June 2021. For this analysis, further attributes of the detected offshore wind turbines were derived. The most important of these are the height and installed capacity of a turbine. The turbine height was calculated by a radargrammetric analysis of the previously detected Sentinel-1 signal and then used to statistically model the installed capacity. The results show that in June 2021, 8,885 offshore wind turbines with a total capacity of 40.6 GW were installed worldwide. The largest installed capacities are in the EU (15.2 GW), China (14.1 GW) and the United Kingdom (10.7 GW). From July 2016 to June 2021, China has expanded 13 GW of offshore wind energy infrastructure. The EU has installed 8 GW and the UK 5.8 GW of offshore wind energy infrastructure in the same period. This temporal analysis shows that China was the main driver of the expansion of the offshore wind energy sector in the period under investigation.
The derived data set for the description of the offshore wind energy sector was made publicly available. It is thus freely accessible to all decision-makers and stakeholders involved in the development of offshore wind energy projects. Especially in the scientific context, it serves as a database that enables a wide range of investigations. Research questions regarding offshore wind turbines themselves as well as the influence of the expansion in the coming decades can be investigated. This supports the imminent and urgently needed expansion of offshore wind energy in order to promote sustainable expansion in addition to the expansion targets that have been set.
The investigation of the Earth system and interplays between its components is of utmost importance to enhance the understanding of the impacts of global climate change on the Earth's land surface. In this context, Earth observation (EO) provides valuable long-term records covering an abundance of land surface variables and, thus, allowing for large-scale analyses to quantify and analyze land surface dynamics across various Earth system components. In view of this, the geographical entity of river basins was identified as particularly suitable for multivariate time series analyses of the land surface, as they naturally cover diverse spheres of the Earth. Many remote sensing missions with different characteristics are available to monitor and characterize the land surface. Yet, only a few spaceborne remote sensing missions enable the generation of spatio-temporally consistent time series with equidistant observations over large areas, such as the MODIS instrument.
In order to summarize available remote sensing-based analyses of land surface dynamics in large river basins, a detailed literature review of 287 studies was performed and several research gaps were identified. In this regard, it was found that studies rarely analyzed an entire river basin, but rather focused on study areas at subbasin or regional scale. In addition, it was found that transboundary river basins remained understudied and that studies largely focused on selected riparian countries. Moreover, the analysis of environmental change was generally conducted using a single EO-based land surface variable, whereas a joint exploration of multivariate land surface variables across spheres was found to be rarely performed.
To address these research gaps, a methodological framework enabling (1) the preprocessing and harmonization of multi-source time series as well as (2) the statistical analysis of a multivariate feature space was required. For development and testing of a methodological framework that is transferable in space and time, the transboundary river basins Indus, Ganges, Brahmaputra, and Meghna (IGBM) in South Asia were selected as study area, having a size equivalent to around eight times the size of Germany. These basins largely depend on water resources from monsoon rainfall and High Mountain Asia which holds the largest ice mass outside the polar regions. In total, over 1.1 billion people live in this region and in parts largely depend on these water resources which are indispensable for the world's largest connected irrigated croplands and further domestic needs as well. With highly heterogeneous geographical settings, these river basins allow for a detailed analysis of the interplays between multiple spheres, including the anthroposphere, biosphere, cryosphere, hydrosphere, lithosphere, and atmosphere.
In this thesis, land surface dynamics over the last two decades (December 2002 - November 2020) were analyzed using EO time series on vegetation condition, surface water area, and snow cover area being based on MODIS imagery, the DLR Global WaterPack and JRC Global Surface Water Layer, as well as the DLR Global SnowPack, respectively. These data were evaluated in combination with further climatic, hydrological, and anthropogenic variables to estimate their influence on the three EO land surface variables. The preprocessing and harmonization of the time series was conducted using the implemented framework. The resulting harmonized feature space was used to quantify and analyze land surface dynamics by means of several statistical time series analysis techniques which were integrated into the framework. In detail, these methods involved (1) the calculation of trends using the Mann-Kendall test in association with the Theil-Sen slope estimator, (2) the estimation of changes in phenological metrics using the Timesat tool, (3) the evaluation of driving variables using the causal discovery approach Peter and Clark Momentary Conditional Independence (PCMCI), and (4) additional correlation tests to analyze the human influence on vegetation condition and surface water area.
These analyses were performed at annual and seasonal temporal scale and for diverse spatial units, including grids, river basins and subbasins, land cover and land use classes, as well as elevation-dependent zones. The trend analyses of vegetation condition mostly revealed significant positive trends. Irrigated and rainfed croplands were found to contribute most to these trends. The trend magnitudes were particularly high in arid and semi-arid regions. Considering surface water area, significant positive trends were obtained at annual scale. At grid scale, regional and seasonal clusters with significant negative trends were found as well. Trends for snow cover area mostly remained stable at annual scale, but significant negative trends were observed in parts of the river basins during distinct seasons. Negative trends were also found for the elevation-dependent zones, particularly at high altitudes. Also, retreats in the seasonal duration of snow cover area were found in parts of the river basins. Furthermore, for the first time, the application of the causal discovery algorithm on a multivariate feature space at seasonal temporal scale revealed direct and indirect links between EO land surface variables and respective drivers. In general, vegetation was constrained by water availability, surface water area was largely influenced by river discharge and indirectly by precipitation, and snow cover area was largely controlled by precipitation and temperature with spatial and temporal variations. Additional analyses pointed towards positive human influences on increasing trends in vegetation greenness. The investigation of trends and interplays across spheres provided new and valuable insights into the past state and the evolution of the land surface as well as on relevant climatic and hydrological driving variables. Besides the investigated river basins in South Asia, these findings are of great value also for other river basins and geographical regions.
In the Spessart, a low mountain range in central Germany, a feud during the Middle Ages led to the construction of numerous castles in this region. This study analyzes the mutual influence of (paleo-)relief development and medieval building activity using a geomorphological and geoarchaeological multimethod approach to expand the knowledge of human-environmental interactions during this time. For this purpose, GIS-based terrain analysis and geophysical measurements were conducted and combined with sedimentological information to create 1D-3D models of the subsurface and to assess knowledge of the landscape and relief evolution at various medieval castle and mining sites. The interpretation of all these data led to the answering of numerous site-specific questions on various geomorphological, geoarchaeological, geologic, and archaeological topics that have been explored in this work and have greatly increased our knowledge of each study site. In addition to these key contributions to the archaeological and geomorphological interpretation of individual study sites, a quantification of the anthropogenic influence on the relief development was conducted, a generalized model of the influence was derived, and new methodological and interpretative approaches were developed. Overall, this study links geomorphological/geological and (geo-)archaeological investigations at five medieval sites and delivers important information on human-environmental interactions within the Spessart and beyond.
With accelerating global climate change, the Antarctic Ice Sheet is exposed to increasing ice dynamic change. During 1992 and 2017, Antarctica contributed ~7.6 mm to global sea-level-rise mainly due to ocean thermal forcing along West Antarctica and atmospheric warming along the Antarctic Peninsula (API). Together, these processes caused the progressive retreat of glaciers and ice shelves and weakened their efficient buttressing force causing widespread ice flow accelerations. Holding ~91% of the global ice mass and 57.3 m of sea-level-equivalent, the Antarctic Ice Sheet is by far the largest potential contributor to future sea-level-rise.
Despite the improved understanding of Antarctic ice dynamics, the future of Antarctica remains difficult to predict with its contribution to global sea-level-rise representing the largest uncertainty in current projections. Given that recent studies point towards atmospheric warming and melt intensification to become a dominant driver for future Antarctic ice mass loss, the monitoring of supraglacial lakes and their impacts on ice dynamics is of utmost importance. In this regard, recent progress in Earth Observation provides an abundance of high-resolution optical and Synthetic Aperture Radar (SAR) satellite data at unprecedented spatial and temporal coverage and greatly supports the monitoring of the Antarctic continent where ground-based mapping efforts are difficult to perform. As an automated mapping technique for supraglacial lake extent delineation in optical and SAR satellite imagery as well as a pan-Antarctic inventory of Antarctic supraglacial lakes at high spatial and temporal resolution is entirely missing, this thesis aims to advance the understanding of Antarctic surface hydrology through exploitation of spaceborne remote sensing.
In particular, a detailed literature review on spaceborne remote sensing of Antarctic supraglacial lakes identified several research gaps including the lack of (1) an automated mapping technique for optical or SAR satellite data that is transferable in space and time, (2) high-resolution supraglacial lake extent mappings at intra-annual and inter-annual temporal resolution and (3) large-scale mapping efforts across the entire Antarctic continent. In addition, past method developments were found to be restricted to purely visual, manual or semi-automated mapping techniques hindering their application to multi-temporal satellite imagery at large-scale. In this context, the development of automated mapping techniques was mainly limited by sensor-specific characteristics including the similar appearance of supraglacial lakes and other ice sheet surface features in optical or SAR data, the varying temporal signature of supraglacial lakes throughout the year as well as effects such as speckle noise and wind roughening in SAR data or cloud coverage in optical data. To overcome these limitations, this thesis exploits methods from artificial intelligence and big data processing for development of an automated processing chain for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 satellite imagery. The combination of both sensor types enabled to capture both surface and subsurface lakes as well as to acquire data during cloud cover or wind roughening of lakes. For Sentinel-1, a deep convolutional neural network based on residual U-Net was trained on the basis of 21,200 labeled Sentinel-1 SAR image patches covering 13 Antarctic regions. Similarly, optical Sentinel-2 data were collected over 14 Antarctic regions and used for training of a Random Forest classifier. Optical and SAR classification products were combined through decision-level fusion at bi-weekly temporal scale and unprecedented 10 m spatial resolution. Finally, the method was implemented as part of DLR’s High-Performance Computing infrastructure allowing for an automated processing of large amounts of data including all required pre- and postprocessing steps. The results of an accuracy assessment over independent test scenes highlighted the functionality of the classifiers returning accuracies of 93% and 95% for supraglacial lakes in Sentinel-1 and Sentinel-2 satellite imagery, respectively.
Exploiting the full archive of Sentinel-1 and Sentinel-2, the developed framework for the first time enabled the monitoring of seasonal characteristics of Antarctic supraglacial lakes over six major ice shelves in 2015-2021. In particular, the results for API ice shelves revealed low lake coverage during 2015-2018 and particularly high lake coverage during the 2019-2020 and 2020-2021 melting seasons. On the contrary, East Antarctic ice shelves were characterized by high lake coverage during 2016-2019 and
extremely low lake coverage during the 2020-2021 melting season. Over all six investigated ice shelves, the development of drainage systems was revealed highlighting an increased risk for ice shelf instability. Through statistical correlation analysis with climate data at varying time lags as well as annual data on Southern Hemisphere atmospheric modes, environmental drivers for meltwater ponding were revealed. In addition, the influence of the local glaciological setting was investigated through computation of annual recurrence times of lakes. Over both ice sheet regions, the complex interplay between local, regional and large-scale environmental drivers was found to control supraglacial lake formation despite local to regional discrepancies, as revealed through pixel-based correlation analysis. Local control factors included the ice surface topography, the ice shelf geometry, the presence of low-albedo features as well as a reduced firn air content and were found to exert strong control on lake distribution. On the other hand, regional controls on lake evolution were revealed to be the amount of incoming solar radiation, air temperature and wind occurrence. While foehn winds were found to dictate lake evolution over the API, katabatic winds influenced lake ponding in East Antarctica. Furthermore, the regional near-surface climate was shown to be driven by large-scale atmospheric modes and teleconnections with the tropics. Overall, the results highlight that similar driving factors control supraglacial lake formation on the API and EAIS pointing towards their transferability to other Antarctic regions.
The area northeast of Sudbury, Ontario, is known for one of the largest unexplained geophysical anomalies on the Canadian Shield, the 1,200 km2 Temagami Anomaly. The geological cause of this regional magnetic, conductive and gravity feature has previously been modelled to be a mafic-ultramafic body at relatively great depth (2–15 km) of unknown age and origin, which may or may not be related to the meteorite impact-generated Sudbury Igneous Complex in its immediate vicinity. However, with a profound lack of outcrops and drill holes, the geological cause of the anomaly remains elusive, a genetic link to the 1.85 Ga Sudbury impact event purely speculative.
In search for any potential surface expression of the deep-seated cause of the Temagami Anomaly, this study provides a first, yet comprehensive petrological and geochemical assessment of exotic igneous dykes recently discovered in outcrops above, and drill cores into, the Temagami Anomaly. Based on cross-cutting field relations, petrographic studies, lithogeochemistry, whole-rock Nd-Sr-Pb isotope systematics, and U-Pb geochronology, it was possible to identify, and distinguish between, at least six different groups of igneous dykes: (i) Calc-alkaline quartz diorite dykes related to the 1.85 Ga Sudbury Igneous Complex (locally termed Offset Dykes); (ii) tholeiitic quartz diabase of the regional 2.22 Ga Nipissing Suite/Senneterre Dyke Swarm; (iii) calc-alkaline quartz diabase of the regional 2.17 Ga Biscotasing Dyke Swarm; (iv) alkaline ultrabasic dykes correlated with the 1.88–1.86 Ga Circum-Superior Large Igneous Province (LIP); and (v) aplitic dykes as well as (vi) a hornblende syenite, the latter two of more ambiguous age and stratigraphic position.
The findings presented in this study – the discovery of three new Offset Dykes in particular – offer some unexpected insights into the geology and economic potential of one of the least explored areas of the world-class Sudbury Mining Camp as well as into the nature and distribution of both allochthonous and autochthonous impactites within one of the oldest and largest impact structures known on Earth. Not only do the geometric patterns of dyke (and breccia) distribution reaffirm previous notions of the existence of discrete ring structures in the sense of a ~200-km multi-ring basin, but they provide critical constraints as to the pre-erosional thickness and extent of the impact melt sheet, thus helping to identity new areas for Ni-Cu-PGE exploration. Furthermore, this study provides important insights into the pre-impact stratigraphy and the magmatic evolution of the region in general, which reveals to be much more complex, compositionally divers, and protracted than initially assumed. Of note is the discovery of rocks related to the 2.17 Ga Biscotasing and the 1.88–1.86 Ga Circum-Superior magmatic events, as these were not previously known to occur on the southeast margin of the Superior Craton. Shortly predating the Sudbury impact and being contemporaneous with ore-forming events at Thompson (Manitoba) and Raglan (Cape Smith), these magmatic rocks could provide the missing link between unusual mafic, pre-enriched, crustal target rocks, and the unique metal endowment of the Sudbury Impact Structure.
The actual geological cause of the Temagami Anomaly remains open to debate and requires the downward extension of existing bore holes as well as more detailed geophysical investigations. The hypothesis of a genetic relationship between Sudbury impact event and Temagami Anomaly is neither borne out by any evidence nor particularly realistic, even in case of an oblique impact, and should thus be abandoned. It is instead proposed, based on circumstantial evidence, that the anomaly might be explained by an ultramafic complex of the 1.88–1.86 Ga Circum-Superior LIP.
Landslide susceptibility assessment in the Chiconquiaco Mountain Range area, Veracruz (Mexico)
(2022)
In Mexico, numerous landslides occur each year and Veracruz represents the state with the third highest number of events. Especially the Chiconquiaco Mountain Range, located in the central part of Veracruz, is highly affected by landslides and no detailed information on the spatial distribution of existing landslides or future occurrences is available. This leaves the local population exposed to an unknown threat and unable to react appropriately to this hazard or to consider the potential landslide occurrence in future planning processes.
Thus, the overall objective of the present study is to provide a comprehensive assessment of the landslide situation in the Chiconquiaco Mountain Range area. Here, the combination of a site-specific and a regional approach enables to investigate the causes, triggers, and process types as well as to model the landslide susceptibility for the entire study area.
For the site-specific approach, the focus lies on characterizing the Capulín landslide, which represents one of the largest mass movements in the area. In this context, the task is to develop a multi-methodological concept, which concentrates on cost-effective, flexible and non-invasive methods. This approach shows that the applied methods complement each other very well and their combination allows for a detailed characterization of the landslide.
The analyses revealed that the Capulín landslide is a complex mass movement type. It comprises rotational movement in the upper parts and translational movement in the lower areas, as well as flow processes at the flank and foot area and therefore, is classified as a compound slide-flow according to Cruden and Varnes (1996). Furthermore, the investigations show that the Capulín landslide represents a reactivation of a former process. This is an important new information, especially with regard to the other landslides identified in the study area. Both the road reconstructed after the landslide, which runs through the landslide mass, and the stream causing erosion processes at the foot of the landslide severely affect the stability of the landslide, making it highly susceptible to future reactivation processes. This is particularly important as the landslide is located only few hundred meters from the village El Capulín and an extension of the landslide area could cause severe damage.
The next step in the landslide assessment consists of integrating the data obtained in the site-specific approach into the regional analysis. Here, the focus lies on transferring the generated data to the entire study area. The developed methodological concept yields applicable results, which is supported by different validation approaches.
The susceptibility modeling as well as the landslide inventory reveal that the highest probability of landslides occurrence is related to the areas with moderate slopes covered by slope deposits. These slope deposits comprise material from old mass movements and erosion processes and are highly susceptible to landslides. The results give new insights into the landslide situation in the Chiconquiaco Mountain Range area, since previously landslide occurrence was related to steep slopes of basalt and andesite.
The susceptibility map is a contribution to a better assessment of the landslide situation in the study area and simultaneously proves that it is crucial to include specific characteristics of the respective area into the modeling process, otherwise it is possible that the local conditions will not be represented correctly.
Impacts of climate variability and change on Maize (\(Zea\) \(mays\)) production in tropical Africa
(2022)
Climate change is undeniable and constitutes one of the major threats of the 21st century. It impacts sectors of our society, usually negatively, and is likely to worsen towards the middle and end of the century. The agricultural sector is of particular concern, for it is the primary source of food and is strongly dependent on the weather. Considerable attention has been given to the impact of climate change on African agriculture because of the continent’s high vulnerability, which is mainly due to its low adaptation capac- ity. Several studies have been implemented to evaluate the impact of climate change on this continent. The results are sometimes controversial since the studies are based on different approaches, climate models and crop yield datasets. This study attempts to contribute substantially to this large topic by suggesting specific types of climate pre- dictors. The study focuses on tropical Africa and its maize yield. Maize is considered to be the most important crop in this region. To estimate the effect of climate change on maize yield, the study began by developing a robust cross-validated multiple linear regression model, which related climate predictors and maize yield. This statistical trans- fer function is reputed to be less prone to overfitting and multicollinearity problems. It is capable of selecting robust predictors, which have a physical meaning. Therefore, the study combined: large-scale predictors, which were derived from the principal component analysis of the monthly precipitation and temperature; traditional local-scale predictors, mainly, the mean precipitation, mean temperature, maximum temperature and minimum temperature; and the Water Requirement Satisfaction Index (WRSI), derived from the specific crop (maize) water balance model. The projected maize-yield change is forced by a regional climate model (RCM) REMO under two emission scenarios: high emission scenario (RCP8.5) and mid-range emission scenario (RCP4.5). The different effects of these groups of predictors in projecting the future maize-yield changes were also assessed. Furthermore, the study analysed the impact of climate change on the global WRSI. The results indicate that almost 27 % of the interannual variability of maize production of the entire region is explained by climate variables. The influence of climate predictors on maize-yield production is more pronounced in West Africa, reaching 55 % in some areas. The model projection indicates that the maize yield in the entire region is expected to decrease by the middle of the century under an RCP8.5 emission scenario, and from the middle of the century to the end of the century, the production will slightly recover but will remain negative (around -10 %). However, in some regions of East Africa, a slight increase in maize yield is expected. The maize-yield projection under RCP4.5 remains relatively unchanged compared to the baseline period (1982-2016). The results further indicate that large-scale predictors are the most critical drivers of the global year-to-year maize-yield variability, and ENSO – which is highly correlated with the most important predictor (PC2) – seems to be the physical process underlying this variability. The effects of local predictors are more pronounced in the eastern parts of the region. The impact of the future climate change on WRSI reveals that the availability of maize water is expected to decrease everywhere, except in some parts of eastern Africa.
The detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate prediction. To overcome this difficulty RARIMAE method were evolved. Applications RARIMAE in the literature shows that amalgamating different methods can be an efficient and effective way to improve the forecasts of time series under consideration. With these motivations, attempt have been made to develop a multiple linear regression model (MLR) and a RARIMAE models for forecasting seasonal rainfall in east Africa under the following objectives:
1. To develop MLR model for seasonal rainfall prediction in East Africa.
2. To develop a RARIMAE model for seasonal rainfall prediction in East Africa.
3. Comparison of model's efficiency under consideration
In order to achieve the above objectives, the monthly precipitation data covering the period from 1949 to 2000 was obtained from Climate Research Unit (CRU). Next to that, the first differenced climate indices were used as predictors.
In the first part of this study, the analyses of the rainfall fluctuation in whole Central- East Africa region which span over a longitude of 15 degrees East to 55 degrees East and a latitude of 15 degrees South to 15 degrees North was done by the help of maps. For models’ comparison, the R-squared values for the MLR model are subtracted from the R-squared values of RARIMAE model. The results show positive values which indicates that R-squared is improved by RARIMAE model. On the other side, the root mean square errors (RMSE) values of the RARIMAE model are subtracted from the RMSE values of the MLR model and the results show negative value which indicates that RMSE is reduced by RARIMAE model for training and testing datasets.
For the second part of this study, the area which is considered covers a longitude of 31.5 degrees East to 41 degrees East and a latitude of 3.5 degrees South to 0.5 degrees South. This region covers Central-East of the Democratic Republic of Congo (DRC), north of Burundi, south of Uganda, Rwanda, north of Tanzania and south of Kenya. Considering a model constructed based on the average rainfall time series in this region, the long rainfall season counts the nine months lead of the first principal component of Indian sea level pressure (SLP_PC19) and the nine months lead of Dipole Mode Index (DMI_LR9) as selected predictors for both statistical and predictive model. On the other side, the short rainfall season counts the three months lead of the first principal component of Indian sea surface temperature (SST_PC13) and the three months lead of Southern Oscillation Index (SOI_SR3) as predictors for predictive model. For short rainfall season statistical model SAOD current time series (SAOD_SR0) was added on the two predictors in predictive model. By applying a MLR model it is shown that the forecast can explain 27.4% of the total variation and has a RMSE of 74.2mm/season for long rainfall season while for the RARIMAE the forecast explains 53.6% of the total variation and has a RMSE of 59.4mm/season. By applying a MLR model it is shown that the forecast can explain 22.8% of the total variation and has a RMSE of 106.1 mm/season for short rainfall season predictive model while for the RARIMAE the forecast explains 55.1% of the total variation and has a RMSE of 81.1 mm/season.
From such comparison, a significant rise in R-squared, a decrease of RMSE values were observed in RARIMAE models for both short rainfall and long rainfall season averaged time series. In terms of reliability, RARIMAE outperformed its MLR counterparts with better efficiency and accuracy. Therefore, whenever the data suffer from autocorrelation, we can go for MLR with ARIMA error, the ARIMA error part is more to correct the autocorrelation thereby improving the variance and productiveness of the model.
The Antarctic Ice Sheet stores ~91% of the global ice volume which is equivalent to a sea-level rise of 58.3 meters. Recent disintegration events of ice shelves and retreating glaciers along the Antarctic Peninsula and West Antarctica indicate the current vulnerable state of the Antarctic Ice Sheet. Glacier tongues and ice shelves create a safety band around Antarctica with buttressing effects on ice discharge. Current decreases in glacier and ice shelf extent reduce the effective buttressing forces and increase ice discharge of grounded ice. The consequence is a higher contribution to sea-level rise from the Antarctic Ice Sheet. So far, it is unresolved which proportion of Antarctic glacier retreat can be attributed to climate change and which part to the natural cycle of growth and decay in the lifetime of a glacier. The quantitative assessment of the magnitude, spatial extent, distribution, and dynamics of circum-Antarctic glacier and ice shelf retreat is of utmost importance to monitor Antarctica’s weakening safety band. In remote areas like Antarctica, earth observation provides optimal properties for large-scale mapping and monitoring of glaciers and ice shelves. Nowadays, the variety of available satellite sensors, technical advancements regarding spatial resolution and revisit times, as well as open satellite data archives create an ideal basis for monitoring calving front change. A systematic review conducted within this thesis revealed major gaps in the availability of glacier and ice shelf front position measurements despite the improved satellite data availability. The previously limited availability of satellite imagery and the time-consuming manual delineation of calving fronts did neither allow a circum-Antarctic assessment of glacier retreat nor the assessment of intra-annual changes in glacier front position. To advance the understanding of Antarctic glacier front change, this thesis presents a novel automated approach for calving front extraction and explores drivers of glacier retreat.
A comprehensive review of existing methods for glacier front extraction ascertained the lack of a fully automatic approach for large-scale monitoring of Antarctic calving fronts using radar imagery. Similar backscatter characteristics of different ice types, seasonally changing backscatter values, multi-year sea ice, and mélange made it challenging to implement an automated approach with traditional image processing techniques. Therefore, the present abundance of satellite data is best exploited by integrating recent developments in big data and artificial intelligence (AI) research to derive circum-Antarctic calving front dynamics. In the context of this thesis, the novel AI-based framework “AntarcticLINES” (Antarctic Glacier and Ice Shelf Front Time Series) was created which provides a fully automated processing chain for calving front extraction from Sentinel-1 imagery. Open access Sentinel-1 radar imagery is an ideal data source for monitoring current and future changes in the Antarctic coastline with revisit times of less than six days and all-weather imaging capabilities. The developed processing chain includes the pre-processing of dual-polarized Sentinel-1 imagery for machine learning applications. 38 Sentinel-1 scenes were used to train the deep learning architecture U-Net for image segmentation. The trained weights of the neural network can be used to segment Sentinel-1 scenes into land ice and ocean. Additional post-processing ensures even more accurate results by including morphological filtering before extracting the final coastline. A comprehensive accuracy assessment has proven the correct extraction of the coastline. On average, the automatically extracted coastline deviates by 2-3 pixels (93 m) from a manual delineation. This accuracy is in range with deviations between manually delineated coastlines from different experts.
For the first time, the fully automated framework AntarcticLINES enabled the extraction of intra-annual glacier front fluctuations to assess seasonal variations in calving front change. Thereby, for example, an increased calving frequency of Pine Island Glacier and a beginning disintegration of Glenzer Glacier were revealed. Besides, the extraction of the entire Antarctic coastline for 2018 highlighted the large-scale applicability of the developed approach. Accurate results for entire Antarctica were derived except for the Western Antarctic Peninsula where training imagery was not sufficient and should be included in future studies.
Furthermore, this dissertation presents an unprecedented record of circum-Antarctic calving front change over the last two decades. The newly extracted coastline for 2018 was compared to previous coastline products from 2009 and 1997. This revealed that the Antarctic Ice Sheet shrank 29,618±1193 km2 in extent between 1997-2008 and gained an area of 7,108±1029 km2 between 2009-2018. Glacier retreat concentrated along the Antarctic Peninsula and West Antarctica. The only East Antarctic coastal sector primarily experiencing calving front retreat was Wilkes Land in 2009-2018. Finally, potential drivers of circum-Antarctic glacier retreat were identified by combining data on glacier front change with changes in climate variables. It was found that strengthening westerlies, snowmelt, rising sea surface temperatures, and decreasing sea ice cover forced glacier retreat over the last two decades. Relative changes in mean air temperature could not be identified as a driver for glacier retreat and further investigations on extreme events in air temperature are necessary to assess the effect of atmospheric forcing on frontal retreat. The strengthening of all identified drivers was closely connected to positive phases of the Southern Annular Mode (SAM). With increasing greenhouse gases and ozone depletion, positive phases of SAM will occur more often and force glacier retreat even further in the future.
Within this thesis, a comprehensive review on existing Antarctic glacier and ice shelf front studies was conducted revealing major gaps in Antarctic calving front records. Therefore, a fully automated processing chain for glacier and ice shelf front extraction was implemented to track circum-Antarctic calving front fluctuations on an intra-annual basis. The large-scale applicability was certified by presenting two decades of circum-Antarctic calving front change. In combination with climate variables, drivers of recent glacier retreat were identified. In the future, the presented framework AntarcticLINES will greatly contribute to the constant monitoring of the Antarctic coastline under the pressure of a changing climate.
Availability of water and desiccation of important water reservoirs is a vital challenge in semi-arid to arid climates with growing economy and population. Low quantities of precipitation and high evaporation rates leave the water supply vulnerable to human activity and climatic variations. Endorheic basins of Northern Iran were hydrologically landlocked within geological timescales and thus bear evidence of past variations of water resources in generations of water related landforms, like abandoned lake level shorelines, alluvial fans and stream terraces. Understanding the development of these landforms reveals crucial information about past water reservoirs and landscape history.
This study offers a comprehensive approach on understanding the geomorphological development of the landscape throughout Late Pleistocene and Holocene times. It integrates remote sensing and geographic information system analysis, with geomorphological and stratigraphical mapping fieldwork and detailed sedimentological investigations.
The work shows the importance of analytical geomorphological mapping for delineating stratigraphic units of the Iranian Quaternary. Thus, several phases of drying and lake level retreat were identified in parallel geoarchives and could be dated to a time span from today to Late Pleistocene. The findings link the fate of the citizens of the ancient city of "Tepe Hissar" to their access to water and to the power of geomorphological processes, which started changing their environment.
Periglacial environments are facing dramatic changes. Warming air temperatures and strong snow cover variations fundamentally affect landforming processes in this hotspot region of Climate Change. But before we can assess the response of landform development to a changing climate, we need to enhance our understanding of the internal structure of those landforms. Within this study, a broad scope of landform types from alpine and subarctic regions is investigated: rock glaciers, solifluction lobes, palsas and patterned ground. By using the geophysical methods 2-D and 3-D ERI, as well as GPR surveying, structural differences and similarities between landform units of different or the same landform types are highlighted. This enables a reconstruction of their past and a projection of their future development.
Worldwide, cold regions are undergoing significant alterations due to climate change. Snow, the most widely distributed cold region component, is highly sensitive to climate change. At the same time, snow itself profoundly impacts the Earth’s energy budget, biodiversity, and natural hazards, as well as hydropower management, freshwater management, and winter tourism/sports. Large parts of the cold regions in Europe are mountain areas, which are densely populated because of the various ecosystem services and socioeconomic well-being in mountains. At present, severe consequences caused by climate change have been observed in European mountains and their surrounding areas. Yet, large knowledge gaps hinder the development of effective regional and local adaptation strategies. Long-term and evidence-based regional studies are urgently needed to enhance the comprehension of regional responses to climate change.
Earth Observation (EO) provides long-term consistent records of the Earth’s surface. It is a great alternative and/or supplement to conventional in-situ measurements which are usually time-consuming, cost-intensive and logistically demanding, particularly for the poor accessibility of cold regions. With the assistance of EO, land surface dynamics in cold regions can be observed in an objective, repeated, synoptic and consistent way. Thanks to free and open data policies, long-term archives such as Landsat Archive and Sentinel Archive can be accessed free-of-charge. The high- to medium-resolution remote sensing imagery from these freely accessible archives gives EO-based time series datasets the capability to depict snow dynamics in European mountains from the 1980s to the present. In order to compile such a dataset, it is necessary to investigate the spatiotemporal availability of EO data, and develop a spatiotemporally transferable framework from which one can investigate snow dynamics.
Among the available EO image archives, the Landsat Archive has the longest uninterrupted records of the Earth’s land surface. Furthermore, its 30 m spatial resolution fulfils the requirements for snow monitoring in complex terrains. Landsat data can yield a time series of snow dynamics in mountainous areas from 1984 to the present. However, severe Landsat data gaps have occurred across certain regions of Europe. Moreover, the Landsat Level 1 Precision and Terrain (L1TP) data is scarcer (up to 50% less) in high-latitude mountainous areas than in low-latitude mountainous areas. Given the abovementioned facts, the Regional Snowline Elevation (RSE) is selected to characterize the snow dynamics in mountainous areas, as it can handle cloud obstructions in the optical images. In this thesis, I present a five-step framework to derive and densify RSE time series in European mountains, i.e. (1) pre-processing, (2) snow detection, (3) RSE retrieval, (4) time series densification, and (5) Regional Snowline Retreat Curve (RSRC) production.
The results of the intra-annual RSE variations show a uniquely high variation in the beginning of the ablation seasons in the Alpine catchment Tagliamento, mainly toward higher elevation. As for inter-annual variations of RSE, median RSE increases in all selected catchments, with an average speed of around 4.66 m ∙ a−1 (median) and 5.87 m ∙ a−1 (at the beginning of the ablation season). The fastest significant retreat is observed in the catchment Drac (10.66 m ∙ a−1, at the beginning of the ablation season), and the slowest significant retreat is observed in the catchment Uzh (1.74 m ∙ a−1, at the beginning of the ablation season). The increase of RSEs at the beginning of the ablation season is faster than the median RSEs, whose average difference is nearly 1.21 m ∙ a−1, particularly in the catchment Drac (3.72 m ∙ a−1). The results of the RSRCs show a significant rise in RSEs at the beginning of the ablation season, except for the Alpine catchment Alpenrhein and Var, and the Pyrenean catchment Ariege. It indicates that 11.8 and 3.97 degrees Celsius less per year are needed for the regional snowlines to reach the middle point of the RSRC in the Tagliamento and Tysa, respectively. The variation of air temperature is regarded as an example of a potential climate driver in this thesis. The retrieved monthly mean RSEs are highly correlated (mean correlation coefficient "R" ̅ = 0.7) with the monthly temperature anomalies, which are more significant in months with extremely low/high temperature. Another case study that investigates the correlation between river discharges and RSEs is carried out to demonstrate the potential consequences of the derived snowline dynamics. The correlation analysis shows a good correlation between river discharges and RSEs (correlation coefficient, R=0.52).
In this thesis, the developed framework signifies a better understanding of the snow dynamics in mountain areas, as well as their potential triggers and consequences. Nonetheless, an urgent need persists for: (1) validation data to assess long-term snow-related observations based on high-resolution EO data; (2) further studies to reveal interactions between snow and its ambient environment; and (3) regional and local adaptation-strategies coping with climate change. Further studies exploring the above-mentioned research gaps are urgently needed in the future.
Summary
Introduction. Rapid and uncontrolled industrialisation and urbanisation in most developing countries are resulting in land, air and water pollution at rates that the natural environment cannot fully renew. These contemporary environmental issues have attracted local, national and international attention. The problem of urban garbage management is associated with rapid population growth in developing countries. These are pertinent environmental crises of sustainability and sanitation in Sub-Saharan Africa and other Third World countries. Despite efforts of the various tiers of government (the case of Nigeria with three tiers: Federal, State and Local governments) in managing solid waste in urban centres, it is still overflowing open dumpsites, litters streets and encroaches into water bodies. These affect the quality of urban living conditions and the natural environment.
Sub-Saharan and other developing countries are experiencing an upsurge in the accumulation and the diversity of waste including E-waste, waste agricultural biomass and waste plastics. The need for effective, sustainable and efficient management of waste through the application of 3Rs principle (Reduce, Reuse, and Recycle) is an essential element for promoting sustainable patterns of consumption and production. This study examined waste management in Imo State, Nigeria as an aspect correlated to the sustainability of its environment.
Materials and methods. To analyse waste management as a correlate of environmental sustainability in Sub-Saharan Africa, Imo State, in eastern Nigeria was chosen as a study area. Issues about waste handling and its impact on the environment in Imo have been reported since its creation in 1976; passing through the State with the cleanest State capital in 1980 to a ‘dunghill’ in 2013 and a ‘garbage capital’ on October 1, 2016. Within this State, three study sites were selected – Owerri metropolis (the State capital) Orlu and Okigwe towns. At these sites, households, commercial areas, accommodation and recreational establishments and schools, as well as dumpsites were investigated to ascertain the composition, quantity, distribution, handling patterns of waste in relation to the sustainability of the State’s environment. This was done conveniently but randomly through questionnaires, interviews, focus group discussions and non-participant observation; these were all heralded by a detailed deskwork. Data were entered using Microsoft Office Excel and were explored and analysed using the Statistical Package for Social Sciences - SPSS.
Data were made essentially of categorical variables and were analysed using descriptive statistics. The association between categorical variables was measured using Cramer’s V the Chi-Square that makes the power and the reliability of the test. Cramer’s V is a measure of association tests directly integrated with cross-tabulation. The Chi-Square test of equal proportions was used to compare proportions for significant differences at 0.05 levels. The statistical package - the Epi Info 6.04d was also used since a contingency table had to be created from several sub-outputs and determine the extent of association between the row and column categories.
The scale variable ‘quantity of waste generated’ was described using measures of central tendency. It was screened for normality using the Kolmogorov-Smirnov and Shapiro-Wilk tests for normality; in all context, the normality assumption was violated (P<0.05). Five null hypotheses were tested using Logistic Regression model. The explanatory power of individual conceptual component was calculated using the Cox & Snell R2 and that of individual indicators was also appraised using the Likelihood Ratio test.
In the context of this work, the significance of the variability explained by the model (baseline model) was appraised using the Omnibus Tests of Model Coefficients, the magnitude of this variability explained by the model using the Cox & Snell R2 and the effects of individual predictors using the Likelihood Ratio test.
Qualitatively, data from open-ended items, observations and interviews were analysed using the process of thematic analysis whereby concepts or ideas were grouped under umbrella terms or keywords. The results were presented using tables, charts, graphs, photos and maps.
Findings and discussions. The total findings and analyses indicated that proper waste handling in Imo State, Nigeria has a positive impact on the environment. This was assessed by the community’s awareness of waste management via sources like the radio and the TV, their education on waste management and schools’ integration of environmental education in their program. Although most community members perceived the State’s environment as compared to it about 10 years’ back has worsened, where they were conscious of proper waste handling measures, the environment was described to be better. This influence of environmental awareness and education on environmental sustainability appraised using Logistic Regression Model, portrayed a significant variability (Omnibus Tests of Model Coefficients: χ2=42.742; P=0.014), inferring that environmental awareness and education significantly predict environmental sustainability.
The findings also revealed that organic waste generation spearheaded amongst other waste types like paper, plastic, E-waste, metal, textile and glass. While waste pickers always sorted paper, plastics, aluminium and metal, some of them also sorted out textile and glass. Statistically (P<0.05), in situations where waste was least generated (i.e., 1-2kg per day), community members maintained that the environmental quality was better in comparison to 10 years’ back. Waste items like broken glass and textile as well as the remains of E-waste after the extraction of copper and brass were not sorted for and these contributed more to environmental degradation.
Similarly, the influence of wealth on environmental sustainability was appraised using Logistic Regression Model including development index related indicators like education, occupation, income and the ability to pay for waste disposal. Harmonising the outcome, farmers, who were mostly the least educated claimed to notice more environmental improvement. In addition, those who did not agree to pay for waste disposal who were mostly those with low income (less than 200,000 Naira, i.e. about 620 Euros monthly) perceived environmental improvement more than those with income above 200,000 Naira. This irony can be attributed to the fact that those with low educational backing lack the capacity to appreciate environmental sustainability pointers well as compared to those with a broader educational background with critical thinking.
The employment and poverty reduction opportunities pertaining to waste management on environmental sustainability was appraised using qualitative thematic analysis. All community members involved in sorting, buying and selling of waste items had no second job. They attested that the money earned from their activities sustained their livelihood and families. Some expressed love for the job, especially as they were their own masters. Waste picking and trading in waste items are offering employment opportunities to many communities around the world. For instance, in the waste recycling, waste composting, waste-to-energy plants and die Stadtreiniger in Würzburg city. The workers in these enterprises have jobs as a result of waste.
Waste disposal influence on environmental sustainability was appraised using the Binary Logistic Regression Model and the variability explained by the model was significant. The validity was also supported by the Wald statistics (P<0.05), which indicates the effect of the predictors is significant. Environmental sustainability was greatly reliant on indicators like the frequency at which community members emptied their waste containers; how/where waste is disposed of, availability of disposal site or public bin near the house, etc. Imolites who asserted to have public waste bins or disposal sites near their houses maintained that the quality of the State’s environment had worsened as such containers/disposal sites were always stinking as well as had animals and smoke around them. Imolites around disposal sites complained of traits like diarrhoea, catarrh, insect bites, malaria, smoke and polluted air.
Conclusions. The liaison between poor waste management strategies and the sustainability of the Imo State environment was considered likely as statistically significant ineffectiveness, lack of awareness, poverty, insufficient and unrealistic waste management measures were found in this study area. In these situations, the environment was said to have not improved. Such inadequacies in the handling of generated waste did not only expose the citizenry to health dangers but also gave rise to streets and roads characterized by filth and many unattended disposal sites unleashing horrible odour to the environment and attracting wild animals. This situation is not only prevalent in Imo State, Nigeria but in many Sub-Saharan cities.
Future Perspectives. To improve the environment in Sub-Saharan Africa, it is imperative to practice an inclusive and integrated sustainable waste management system. The waste quantity in this region is fast growing, especially food/organic waste. The region should aim at waste management laws and waste reduction strategies, which will help save and produce more food that it really needs. Waste management should be dissociated from epidemic outbreaks like cholera, typhoid, Lassa fever and malaria, whose vectors thrive in filthy environments. Water channels and water bodies should not be waste disposal channels or waste disposal sites.
West Africa is one of the fastest growing regions in the world with annual population growth rates of more than three percent for several countries. Since the 1950s, West Africa experienced a fivefold increase of inhabitants, from 71 to 353 million people in 2015 and it is expected that the region’s population will continue to grow to almost 800 million people by the year 2050. This strong trend has and will have serious consequences for food security since agricultural productivity is still on a comparatively low level in most countries of West Africa. In order to compensate for this low productivity, an expansion of agricultural areas is rapidly progressing. The mapping and monitoring of agricultural areas in West Africa is a difficult task even on the basis of remote sensing. The small scale extensive farming practices with a low level of agricultural inputs and mechanization make the delineation of cultivated land from other land cover and land use (LULC) types highly challenging. In addition, the frequent cloud coverage in the region considerably decreases the availability of earth observation datasets. For the accurate mapping of agricultural area in West Africa, high temporal as well as spatial resolution is necessary to delineate the small-sized fields and to obtain data from periods where different LULC types are distinguishable. However, such consistent time series are currently not available for West Africa. Thus, a spatio-temporal data fusion framework was developed in this thesis for the generation of high spatial and temporal resolution time series.
Data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) enjoyed increasing popularity during recent years but they have hardly been used for the application on larger scales. In order to make it applicable for this purpose and to increase the input data availability, especially in cloud-prone areas such as West Africa, the ESTARFM framework was developed in this thesis introducing several enhancements. An automatic filling of cloud gaps was included in the framework in order to use even partly cloud-covered Landsat images for the fusion without producing gaps on the output images. In addition, the ESTARFM algorithm was improved to automatically account for regional differences in the heterogeneity of the study region. Further improvements comprise the automation of the time series generation as well as the significant acceleration of the processing speed through parallelization. The performance of the developed ESTARFM framework was tested by fusing an 8-day NDVI time series from Landsat and MODIS data for a focus area of 98,000 km² in the border region between Burkina Faso and Ghana. The results of this test show the capability of the ESTARFM framework to accurately produce high temporal resolution time series while maintaining the spatial detail, even in such a heterogeneous and cloud-prone region.
The successfully tested framework was subsequently applied to generate consistent time series as the basis for the mapping of agricultural area in Burkina Faso for the years 2001, 2007, and 2014. In a first step, high temporal (8-day) and high spatial (30 m) resolution NDVI time series for the entire country and the three years were derived with the ESTARFM framework. More than 500 Landsat scenes and 3000 MODIS scenes were automatically processed for this purpose. From the fused ESTARFM NDVI time series, phenological metrics were extracted and together with the single time steps of NDVI served as input for the delineation of rainfed agricultural areas, irrigated agricultural areas and plantations. The classification was conducted with the random forest algorithm at a 30 m spatial resolution for entire Burkina Faso and the three years 2001, 2007, and 2014. For the training and validation of the classifier, a randomly sampled reference dataset was generated from Google Earth images based on expert knowledge of the region. The overall classification accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the developed methodology. The resulting maps show an expansion of agricultural area of 91% from about 61,000 km² in 2001 to 116,900 km² in 2014. While rainfed agricultural areas account for the major part of this increase, irrigated areas and plantations also spread considerably. Especially the expansion of irrigation systems and plantation area can be explained by the promotion through various national and international development projects. The increase of agricultural areas goes in line with the rapid population growth in most of Burkina Faso’s provinces which still had available land resources for an expansion of agricultural area. An analysis of the development of agricultural areas in the vicinity of protected areas highlighted the increased human pressure on these reserves. The protection of the remnant habitats for flora and fauna while at the same time improving food security for a rapidly growing population, are the major challenges for the region in the future.
The developed ESTARFM framework showed great potential beyond its utilization for the mapping of agricultural area. Other large-scale research that requires a sufficiently high temporal and spatial resolution such as the monitoring of land degradation or the investigation of land surface phenology could greatly benefit from the application of this framework.
As a cradle of ancient Chinese civilization, the Yellow River Basin has a very long human-environment interrelationship, where early anthropogenic activities re- sulted in large scale landscape modifications. Today, the impact of this relationship
has intensified further as the basin plays a vital role for China’s continued economic
development. It is one of the most densely-populated, fastest growing, and most dynamic
regions of China with abundant natural and environmental resources providing a livelihood for almost 190 million people. Triggered by fundamental economic reforms, the
basin has witnessed a spectacular economic boom during the last decades and can be
considered as an exemplary blueprint region for contemporary dynamic Global Change
processes occurring throughout the country, which is currently transitioning from an
agrarian-dominated economy into a modern urbanized society. However, this resourcesdemanding growth has led to profound land use changes with adverse effects on the Yellow
River social-ecological systems, where complex challenges arise threatening a long-term
sustainable development.
Consistent and continuous remote sensing-based monitoring of recent and past land
cover and land use change is a fundamental requirement to mitigate the adverse impacts
of Global Change processes. Nowadays, technical advancement and the multitude of
available satellite sensors, in combination with the opening of data archives, allow the
creation of new research perspectives in regional land cover applications over heterogeneous landscapes at large spatial scales. Despite the urgent need to better understand the
prevailing dynamics and underlying factors influencing the current processes, detailed
regional specific land cover data and change information are surprisingly absent for this
region.
In view of the noted research gaps and contemporary developments, three major objectives are defined in this thesis. First (i), the current and most pressing social-ecological
challenges are elaborated and policy and management instruments towards more sustainability are discussed. Second (ii), this thesis provides new and improved insights on
the current land cover state and dynamics of the entire Yellow River Basin. Finally (iii),
the most dominant processes related to mining, agriculture, forest, and urban dynamics
are determined on finer spatial and temporal scales.
The complex and manifold problems and challenges that result from long-term abuse
of the water and land resources in the basin have been underpinned by policy choices,
cultural attitude, and institutions that have evolved over centuries in China. The tremendous economic growth that has been mainly achieved by extracting water and exploiting
land resources in a rigorous, but unsustainable manner, might not only offset the economic benefits, but could also foster social unrest. Since the early emergence of the first Chinese dynasties, flooding was considered historically as a primary issue in river management and major achievements have been made to tame the wild nature of the Yellow
River. Whereas flooding is therefore largely now under control, new environmental and
social problems have evolved, including soil and water pollution, ecological degradation,
biodiversity decline, and food security, all being further aggravated by anthropogenic
climate change. To resolve the contemporary and complex challenges, many individual
environmental laws and regulations have been enacted by various Chinese ministries.
However, these policies often pursue different, often contradictory goals, are too general
to tackle specific problems and are usually implemented by a strong top-down approach.
Recently, more flexible economic and market-based incentives (pricing, tradable permits,
investments) have been successfully adopted, which are specifically tailored to the respective needs, shifting now away from the pure command and regulating instruments.
One way towards a more holistic and integrated river basin management could be the
establishment of a common platform (e.g. a Geographical Information System) for data
handling and sharing, possibly operated by the Yellow River Basin Conservancy Commission (YRCC), where available spatial data, statistical information and in-situ measures
are coalesced, on which sustainable decision-making could be based. So far, the collected
data is hardly accessible, fragmented, inconsistent, or outdated.
The first step to address the absence and lack of consistent and spatially up-to-date
information for the entire basin capturing the heterogeneous landscape conditions was
taken up in this thesis. Land cover characteristics and dynamics were derived from
the last decade for the years 2003 and 2013, based on optical medium-resolution hightemporal MODIS Normalized Differenced Vegetation Index (NDVI) time series at 250 m.
To minimize the inherent influence of atmospheric and geometric interferences found in
raw high temporal data, the applied adaptive Savitzky-Golay filter successfully smoothed
the time series and substantially reduced noise. Based on the smoothed time series
data, a large variety of intra-annual phenology metrics as well as spectral and multispectral annual statistics were derived, which served as input variables for random
forest (RF) classifiers. High quality reference data sets were derived from very high
resolution imagery for each year independently of which 70 % trained the RF models. The
accuracy assessments for all regionally specific defined thematic classes were based on the
remaining 30 % reference data split and yielded overall accuracies of 87 % and 84 % for
2003 and 2013, respectively. The first regional adapted Yellow River Land Cover Products
(YRB LC) depict the detail spatial extent and distribution of the current land cover status
and dynamics. The novel products overall differentiate overall 18 land cover and use
classes, including classes of natural vegetation (terrestrial and aquatic), cultivated classes,
mosaic classes, non-vegetated, and artificial classes, which are not presented in previous
land cover studies so far.
Building on this, an extended multi-faceted land cover analysis on the most prominent
land cover change types at finer spatial and temporal scales provides a better and more
detailed picture of the Yellow River Basin dynamics. Precise spatio-temporal products
about mining, agriculture, forest, and urban areas were examined from long-trem Landsat
satellite time series monitored at annual scales to capture the rapid rate of change in four
selected focus regions. All archived Landsat images between 2000 and 2015 were used to
derive spatially continuous spectral-temporal, multi-spectral, and textural metrics. For
each thematic region and year RF models were built, trained and tested based on a stablepixels reference data set. The automated adaptive signature (AASG) algorithm identifies those pixels that did not change between the investigated time periods to generate a
mono-temporal reference stable-pixels data set to keep manual sampling requirements
to a minimum level. Derived results gained high accuracies ranging from 88 % to 98 %.
Throughout the basin, afforestation on the Central Loess Plateau and urban sprawl are
identified as most prominent drivers of land cover change, whereas agricultural land
remained stable, only showing local small-scale dynamics. Mining operations started in
2004 on the Qinghai-Tibet Plateau, which resulted in a substantial loss of pristine alpine
meadows and wetlands.
In this thesis, a novel and unique regional specific view of current and past land cover
characteristics in a complex and heterogeneous landscape was presented by using a
multi-source remote sensing approach. The delineated products hold great potential for
various model and management applications. They could serve as valuable components
for effective and sustainable land and water management to adapt and mitigate the
predicted consequences of Global Change processes.
The contact of hot melt with liquid water - called Molten Fuel Coolant Interaction (MFCI) - can result in vivid explosions. Such explosions can occur in different scenarios: in steel or powerplants but also in volcanoes. Because of the possible dramatic consequences of such explosions an investigation of the explosion process is necessary.
Fundamental basics of this process are already discovered and explained, such as the frame conditions for these explosions. It has been shown that energy transfer during an MFCI-process can be very high because of the transfer of thermal energy caused by positive feedback mechanisms.
Up to now the influence of several varying parameters on the energy transfer and the explosions is not yet investigated sufficiently. An important parameter is the melt temperature, because the amount of possibly transferable energy depends on it. The investigation of this influence is the main aim of this work. Therefor metallic tin melt was used, because of its nearly constant thermal material properties in a wide temperature range. With tin melt research in the temperature range from 400 °C up to 1000 °C are
possible.
One important result is the lower temperature limit for vapor film stability in the experiments. For low melt temperatures up to about 600 °C the vapor film is so unstable that it already can collapse before the mechanical trigger. As expected the transferred thermal energy all in all increases with higher temperatures. Although this effect sometimes is superposed by other influences such as the premix of melt and water, the result is confirmed after a consequent filtering of the remaining influences. This trend is not only recognizable in the amount of transferred energy, but also in the fragmentation of melt or the vaporizing water. But also the other influences on MFCI-explosions showed interesting results in the frame of this work. To perform the experiments the installation and preparation of the experimental Setup in the laboratory were necessary.
In order to compare the results to volcanism and to get a better investigation of the brittle fragmentation
of melt additional runs with magmatic melt were made. In the results the thermal power during energy transfer could be estimated. Furthermore the model of “cooling fragments “ could be usefully applied.
In the 1960s, when most African nations gained their independence after the age of colonialism, several theories and strategies emerged with the goal of "developing" these apparently "underdeveloped" territories. One of the most influential approaches for this task was represented in Julius K. Nyerere´s idea of Ujamaa, the Tanzanian version of African socialism.
Even before the Arusha Declaration established Ujamaa as a national development strategy in 1967, several groups of politicized young farmers took to the empty countryside of Tanzania to implement their own version of cooperative development. From one of these attempts emerged the Ruvuma Development Association (RDA), which organized up to 18 villages in southwestern Tanzania. The RDA became the inspiration for Nyerere´s concretization of Ujamaa and its implementation on national level. Yet, the central state could not replicate the success of the peasants, which was based on voluntariness and intrinsic motivation.
In 2015, this exploratory study has revisited the Region of Ruvuma. Through a case study approach, relying mostly on qualitative methods, new insights into the local history of Ujamaa and its perception have been gathered. In particular, narrative interviews with contemporary witnesses and group interviews with the present-day farmers’ groups have been conducted. Furthermore, NGOs active within the region, as well as regional and local government institutions were among the key stakeholders identified to concretize the local narrative of Ujamaa development. All interviews were analyzed according to the principles of qualitative content analysis. Additionally, individual villager questionnaires were used to achieve a more holistic picture of the local perception of development, challenges and the Ujamaa era.
None of the original Ujamaa groups of the times of the RDA was still operational at the time of research and no case of village-wide organization of collective agriculture could be observed. Nevertheless, in all of the three case study villages, several farmers’ groups (vikundi) were active in organizing development activities for their members. Furthermore, the perception of the Ujamaa era was generally positive throughout all of the case study sites. Yet, there have been significant differences in this perception, based on the village, age, gender and field size of the recipients. Overall, the period of Ujamaa was seen as an inspiration for present-day group activities, and the idea of such activities as a remedy for the developmental challenges of these villages was common among all stakeholders.
This thesis concludes that the positive perception of group activities as a vehicle for village development and the perception of Ujamaa history as a positive asset for the inception and organization of farmers’ groups would be highly beneficial to further attempts to support such development activities. However, the limitations in market access and capital availability for these highly-motivated group members have to be addressed by public and private development institutions. Otherwise, "the smell of Ujamaa" will be of little use for the progress of these villages.
The Kaapvaal Craton hosts a number of large gold deposits (e.g. Witwatersrand Supergroup) which mining companies have exploited at certain stratigraphic positions. It also hosts the largest platinum group element (PGE) deposits (e.g. Bushveld Igneous Complex) which mining companies have exploited in different mineralised layered magmatic zones. In spite of the extensive exploration history in the Kaapvaal Craton, the origin of the Witwatersrand gold deposits and Bushveld Igneous Complex PGE deposits has remained one of the most debated topics in economic geology. The goal of this study was to identify the geochemical characteristics of marine shales in the Barberton, Witwatersrand, and Transvaal supergroups in South Africa in order to make inferences on their sediment provenance and siderophile element endowments. Understanding why some of the Archaean and Proterozoic hinterlands are heavily mineralised, compared to others with similar geological characteristics, will aid in the development of more efficient exploration models. Fresh, unmineralised marine shales from the Barberton (Fig Tree and Moodies groups), Witwatersrand (West Rand and Central Rand groups), and Transvaal (Black Reef Formation and Pretoria Group) supergroups were sampled from drill core and underground mining exposures. Analytical methods, such as X-ray powder diffraction (XRD), optical microscopy, X-ray fluorescence (XRF), inductively coupled plasma optical emission spectroscopy (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and electron microprobe analysis (EMPA) were applied to comprehensively characterise the shales. All of the Au and PGE assays examined the newly collected shale samples.
The Barberton Supergroup shales consist mainly of quartz, illite, chlorite, and albite, with diverse heavy minerals, including sulfides and oxides, representing the minor constituents. The regionally persistent Witwatersrand Supergroup shales consist mainly of quartz, muscovite, and chlorite, and also contain minor constituents of sulfides and oxides. The Transvaal Supergroup shales comprise quartz, chlorite, and carbonaceous material. Major, trace (including rare-earth element) concentrations were determined for shales from the above supergroups to constrain their source and post-depositional evolution. Chemical variations were observed in all the studied marine shales. Results obtained from this study revealed that post-depositional modification of shale chemistry was significant only near contacts with over- and underlying coarser-grained siliciclastic rocks and along cross-cutting faults, veins, and dykes. Away from such zones, the shale composition remained largely unaltered and can be used to draw inferences concerning sediment provenance and palaeoweathering in the source region and/or on intrabasinal erosion surfaces. Evaluation of weathering profiles through sections of the studied supergroups revealed that the shales therein are characterised by high chemical index of alteration (CIA), chemical index of weathering (CIW), and index of compositional variability (ICV), suggesting that the source area was lithologically complex and subject to intense chemical weathering.
A progressive change in the chemical composition was identified, from a dominant ultramafic–mafic source for the Fig Tree Group to a progressively felsic–plutonic provenance for the Moodies Group. The West Rand Group of the Witwatersrand Supergroup shows a dominance of tonalite–trondhjemite–granodiorite and calcalkaline granite sources. Compositional profiles through the only major marine shale unit within the Central Rand Group indicate the progressive unroofing of a granitic source in an otherwise greenstone-dominated hinterland during the course of sedimentation. No plausible likely tectonic setting was obtained through geochemical modelling. However, the combination of the systematic shale chemistry, geochronology, and sedimentology in the Witwatersrand Supergroup supports the hypothesised passive margin setting for the >2.98 to 2.91 Ga West Rand Group, and an active continental margin source for the overlying >2.90 to 2.78 Ga Central Rand Group, along with a foreland basin setting for the latter.
Ultra-low detection limit analyses of gold and PGE concentrations revealed a variable degree of gold accumulation within pristine unmineralised shales. All the studied shales contain elevated gold and PGE contents relative to the upper continental crust, with marine shales from the Central Rand Group showing the highest Au (±9.85 ppb) enrichment. Based on this variation in the provenance of contemporaneous sediments in different parts of the Kaapvaal Craton, one can infer that the siderophile elements were sourced from a fertile hinterland, but concentrated into the marine shales by a combination of different processes. It is proposed that accumulation of siderophile elements in the studied marine shales was mainly controlled by mechanical coagulation and aggregation. These processes involved suspended sediments, fine gold particles, and other trace elements being trapped in marine environments. Mechanical coagulation and aggregation resulted in gold enrichments by 2–3 orders of magnitude, whereas some of the gold in these marine shales can be reconciled by seawater adsorption into sedimentary pyrite.
For the source of gold and PGEs in the studied marine shales in the Kaapvaal Craton, a genetic model is proposed that involves the following:
(1) A highly siderophile elements enriched upper mantle domain, herein referred to as “geochemically anomalous mantle domain”, from which the Kaapvaal crust was sourced. This mantle domain enriched in highly siderophile elements was formed either by inhomogeneous mixing with cosmic material that was added during intense meteorite bombardment of the Hadaean to Palaeoarchaean Earth or by plume-like ascent of relics from the core–mantle boundary. In both cases, elevated siderophile elements concentrations would be expected. The geochemically anomalous mantle domain is likely the ultimate source of the Witwatersrand modified palaeoplacer gold deposits and was tapped again ca. 2.054 Ga during the emplacement of the Bushveld Igneous Complex. Therefore, I propose that there is a genetic link (i.e. common geochemically anomalous mantle source) between the Witwatersrand gold deposits and the younger Bushveld Igneous Complex PGE deposits.
(2) Scavenging of crustal gold by various surface processes such as trapping of gold from Archaean/Palaeoproterozoic river water on the surface of local photosynthesizing cyanobacterial or microbial mats, and reworking of these mats into erosion channels during flooding events.
The above two models complement each other, with model (1) providing a common geological source for the Witwatersrand gold and Bushveld Igneous Complex PGE deposits, and model (2) explaining the processes responsible for Witwatersrand-type gold pre-concentration processes. In sequences such as the Transvaal Supergroup, a less fertile hinterland and/or less reworking of older sediments led to a correspondingly lower gold endowment. These findings indicate temporal distribution of siderophile elements in the upper crust (e.g. marine shales). The overall implications of these findings are that background concentrations of gold and PGEs can be used to target potential exploration areas in other cratons of similar age. This increases the likelihood of finding other Witwatersrand-type gold or Bushveld Igneous Complex-type PGE deposits in other cratons.
11 Conclusion
11.1 Glaze compositions
Glazes from tiles of imposing Islamic buildings and some tableware glazes of the medieval epoch in Central Asia, the Middle East, Asia Minor, and North Africa are analysed regarding their main composition and colouring agents. Three major production recipes can be distinguished, i.e. alkali glazes, alkali lead glazes, and lead glazes. In the work of Tite (2011), Islamic glazes from Egypt, Iran, Iraq, and Syria are subdivided into four groups of composition, being partly consistent with those of this work. The alkali lime glazes with <2 wt% PbO correspond to the alkali glazes, but with higher content of CaO. The second and third group of low lead alkali and lead alkali glazes (2-10 wt% PbO and 10-35 wt% PbO) can be subsumed to the alkali lead group described here. Tite´s high lead group has PbO contents >35 wt% and is comparable to the lead glazes (>30 wt% PbO) of this study. The lead and the alkali oxides serve as a flux for the lowering the melting point.
In the interaction of ceramic body and glaze, primarily an influence from Si, Al, and K is observed in the line scans from the cross section of ceramic and glaze. However, the input of ceramic material doesn’t seem to be critical for the classification of glazes according to their alkali and alkali lead compositions.
In every epoch and locality, except of the Ilkhanate dynasty in Iran, lead glaze samples can be verified. This is also observed in previous investigations e.g. from medieval Iraq, Jordan and Iran (McCarthy, 1996; Al-Saad, 2002; Holakooei et al., 2014). In the Moroccan and Bulgarian glazes, lead seems to be the only important flux. In part, the lead flux is supplemented by additional alkali contents. The lack of alkali and alkali lead glazes in Bulgarian and Moroccan glazes (assuming that the Ottoman alkali lead glazes are imported tableware) seems to affect the regions with Roman-influenced history and with geographical distance to the Near East alkali flux tradition.
For the alkali lead glazes and alkali glazes, the overall characteristic is sodium dominated, although the absolute soda values are in part surprisingly low. Samples from Bukhara, Takht-i-Suleiman and the Turkish localities have the highest, but still moderate Na2O values up to 15 wt%, compared to other analyses from e.g. India (Gill & Rehren, 2011).
The source of the alkali flux is either mineral natron or plant ash. The source can be determined regarding the MgO values, limited to 1.3 wt% in mineral natron and exceeding 2.0 wt% in the case of plant ashes. In the samples of the present study, the K2O component is not suitable for the indication of the flux-relevant alkali source due to its broad scattering. The P2O5 contents are also enhanced in the plant ash compositions but the data set is not sufficient for statistical evaluation. An influence of the ceramic body on the glaze composition is observed only for SiO2, Al2O3, and K2O in quartz frit ceramics with slight K-feldspar content.
The earliest Uzbek tableware glazes from the 10th-11th century (Seljuq period) were generally produced using a lead flux. The same applies to part of the Uzbek tile glazes which were produced between the 13th and 16th century. In Iran, glazes from the 12th century (Khwarezmid period) are lead glazes, but also alkali-fluxed glazes with mineral natron characteristics can be found. Although the production of lead-rich glazes was established from the 8th-9th century on in Iraq, Syria, and Egypt (Henshaw, 2010; Tite et al., 2011), alkali glazes are found in almost all regions except of Bulgaria and Morocco.
Plant ash-fluxed alkali glazes are found in 13th century glazes from Takht-i-Suleiman. The plant ash flux technology is assumed to be continuously used in Mesopotamia, Iran, and Central Asia (Sayre & Smith, 1974; Henderson, 2009), but it could be shown that a parallel use of mineral natron parallel existed in the alkali glaze production from the 12th-15th century from Uzbekistan to Afghanistan. Mineral natron characteristics are also reported by Mason (2004) for Syrian and Iranian alkali glazes on lustre ware of the 8th-14th century. Tile glazes with partly mineral natron compositions are found in the Mughal architectural glazes from the 14th- 17th century from India (Gill et al., 2014).
Alkali and alkali lead tile glazes from Samarkand from the 13th century (Mongolian period) have mineral natron flux characteristics, but samples from the 15th century (Timurid period) show plant ash signature. Alkali fluxed Uzbek glazes from Bukhara from the 16th century (Sheibanid dynasty) are also made by plant ash flux and are subdivided into two groups with high and low sodium oxide content. The Afghan alkali glazes have sodium oxide contents similar to the sodium-poor Uzbek subgroup, which points to a possible exchange of glaze makers or glaze making technology from Uzbekistan and Afghanistan in the 15th-17th century. Regarding the extensive exchange of Timurid craftsmen in Central Asia, this option seems to be even more likely (Golombek, 1996). One sample from the 15th century from Afghanistan with mineral natron reveals that this material was parallel used in these centuries.
Concerning the colouring of the glazes, it has to be distinguished between pigments and colouring ions which are incorporated in the glassy matrix. The colouring agents for translucent glazes are cations of various transition metals. As ions, Co2+ (blue), Cu2+ (green in a lead rich matrix), Fe3+ (brown/black), Mn4+ (brown/black) and Mn3+ (violet) are determined by EPMA. For opaque yellow, white, and turquoise glazes, different pigments were used. The crystalline pigments are investigated by a µ XRD2 device with the result of SnO2, SiO2, and PbSiO4 as whitening agents. PbSiO4 and Pb2Sn2O6 are found in the yellow glaze, from which only the lead tin oxide causes the yellow colour. In the black glazes, different Cr-rich pigments, Cu-Cr-Mn-oxides and iron containing clinopyroxenes are found, even in samples of the same period and region. Cr-rich particles are also detected in two turquoise Afghan glazes from the 15th and 16th century. The use of the ions of Fe, Cu, Co, Cr, and Mn seems to be widely common in the Islamic glazes and corresponds to the described colouring agents in e.g. the study of Tite (2011). The use of opacifying SnO2 particles is widespread as it is reported from different Islamic glazes from Iraq, Iran, Egypt, and Syria (Henshaw, 2010; O´Kane, 2011; Tite, 2011). The colouring agents are known already from former, e.g. Egyptian, Roman and pre-islamic periods, but especially SnO2 pigments became increasingly widespread in the Islamic glazing tradition. The use of yellow and black pigments instead varies already within the buildings from Bukhara from Cr crystals and clino-pyroxenes in the mosque Khoja Zainuddin to a Cu-Cr-Mn-oxide in the madrassa Mir-i Arab of the same epoch.
Regarding the matrix compositions connected with the colouring, a certain assignment within the different locations and epochs can be seen. It is noticeable that e.g. the content of lead in turquoise glazes in Uzbekistan is in the range of 0.0-9.2 wt% Pb, whereas blue glazes are mostly alkali ones with PbO contents <2.0 wt%. The turquoise glazes show, that this restriction is not influenced by any defaults of availability and processability. The assumption of common addition of lead and tin to the glaze, which is already described for Iranian glazes of the 13th century (Allan et al., 1973) cannot be confirmed by correlations of tin and lead oxide in the compositions.
11.2 Portable XRF measurement
With the p-XRF, semi-quantitative information about the major element compositions is generated. The depth of the detectable signals depends on the analysed sample setup. The p-XRF data are collected with the XL3 Hybrid device of the company Analyticon Instruments. In the comparison of p-XRF results of the “mining” program from Uzbek glazes with EPMA results, the same major composition groups can be distinguished. The Moroccan glazes, all lead rich, are measured with the “mining” as well as with the “soil” program, revealing a better performance in the “mining” measurements. The deviations are nevertheless high, because of the high lead contents, which make the calculation of matrix correction difficult.
The measurement of the colouring oxides MnO2, CoO, and CuO is satisfying with the internal calibration of the device and even improved with the “mining” program measurement, if compared to the results of the “soil” program. The measurements of glaze imitations lead to better results than that of bulk glass. This can be attributed to the smoother surface texture.
In spite of the accuracy limits in the measurements of particular elements in glazes, the classification of flux composition into three groups could be confirmed with the p XRF analysis. The measurement precision is therefore sufficient for the semi-quantitative analysis of the flux characteristic of glazes. Especially for the on-site measurement of large sample quantities on historical buildings, the device is a suitable tool.
11.3 Restoration material
The ORMOCER® fulfils the requirements of stability, reversibility, and transparency, which are imposed to a modern restoration material. As pigments, historically coloured glass, cobalt blue, Egyptian blue, lead tin yellow, manganese violet, iron oxide, copper oxide, and cassiterite were used. The metal compounds have higher colour intensities than the pigments of coloured glass. It has to be considered that the proportion of ORMOCER® in the batch must be high enough (70 vol%) to guarantee the ORMOCER® properties of weathering and mechanical stability. The adhesion properties of the ORMOCER® and the homogeneity of the mixture are the best in a fraction of max. 30 vol% particles per ORMOCER®.
With integrated particles, the ORMOCER® G materials show homogeneous coatings, whereas the particles in the ORMCOER® E show more agglomeration. In the sedimentation and weathering experiments, the use of an ultrasonic finger in combination with a roller mill is favourable compared to the treatment with bead grinding mill. The treatments with ultrasonic finger and roller mill result in less sedimentation and better adhesion of the dispersions. The treatment of the dispersions in the bead grinding mill does not result in sufficient adhesion, certainly due to the sedimentation behaviour and a congregation of particles on the bottom of the coating.
The modification of dispersed nano-particles by 3-methacryl-oxypropyltrimethoxysilan leads to a further homogenization in the sedimentation tests. It is therefore approved for the use in coloured glaze supplements. In weathered coatings of nano-particle compounds, the surface modification shows certainly no enhancement of stability.
The treatment of pigmented coatings with an additional layer of pure ORMOCER® results in a bright and transparent appearing, which is closer to the original optical appearance of the glaze. A long-time test application on a historical building will be the next step to validate the suitability of the restoration material.
The global-local sustainable development and climate change adaptation policy, and the emerging political discourse on the value of local Adaptation, have positioned the local institutions and their governance space within the strategic enclaves of multilevel governance system. Such shifts have transformed the context for sustainable Nature Based Tourism (NBT) development and adaptation in Nepal in general, and its protected areas, in particular. The emerging institutional adaptation discourse suggests on the need to link tourism development, adaptation and governance within the sustainability concept, and also to recognize the justice and inclusive dimensions of local adaptation. However, sociological investigation of institutional adaptation, particularly at the interface between sustainability, justice and inclusive local adaptation is an undertheorized research topic.
This exploratory study examined the sociological process of the institutional adaptation, especially the social resilience and adaptive governance capacities of the NBT institutions, in 7 Village Development Committees of the Mustang district, a popular destination in the Annapurna Conservation Area, Nepal. Using the sphere (a dynamic social space concept) and quality of governance as the analytical framework, the integrative adaptation as the methodological approach and the case study action research method, the study investigated and generated a holistic picture on the state of the social resilience and adaptive governance capacities of the NBT institutions.
The findings show institutional social resilience capacities to be contingent on socio-political construction of adaptation knowledge and power. Factors influencing such constructions among NBT institutions include: the site and institutions specific political, economic and environmental dispositions; the associated socio-political processes of knowledge constructions and volition action; and the social relationships and interaction, operating within the spheres and at multiple governance levels. The adaptive governance capacities hinge on the institutional arrangements, the procedural aspects of adaptation governance and the governmentality. These are reflective of the diverse legal frameworks, the interiority perspective of the decision making and governance practices of the NBT institutions.
In conclusion, it is argued that effective local adaptation in the Mustang district is contingent on the adaptation and institutional dynamics of the NBT institutions, consisting of the cognitive, subjective, process and procedural aspects of the adaptation knowledge production and its use.
Remote sensing for disease risk profiling: a spatial analysis of schistosomiasis in West Africa
(2014)
Global environmental change leads to the emergence of new human health risks. As a consequence, transmission opportunities of environment-related diseases are transformed and human infection with new emerging pathogens increase. The main motivation for this study is the considerable demand for disease surveillance and monitoring in relation to dynamic environmental drivers. Remote sensing (RS) data belong to the key data sources for environmental modelling due to their capabilities to deliver spatially continuous information repeatedly for large areas with an ecologically adequate spatial resolution.
A major research gap as identified by this study is the disregard of the spatial mismatch inherent in current modelling approaches of profiling disease risk using remote sensing data. Typically, epidemiological data are aggregated at school or village level. However, these point data do neither represent the spatial distribution of habitats, where disease-related species find their suitable environmental conditions, nor the place, where infection has occurred. As a consequence, the prevalence data and remotely sensed environmental variables, which aim to characterise the habitat of disease-related species, are spatially disjunct.
The main objective of this study is to improve RS-based disease risk models by incorporating the ecological and spatial context of disease transmission. Exemplified by the analysis of the human schistosomiasis disease in West Africa, this objective includes the quantification of the impact of scales and ecological regions on model performance.
In this study, the conditions that modify the transmission of schistosomiasis are reviewed in detail. A conceptual underpinning of the linkages between geographical RS measures, disease transmission ecology, and epidemiological survey data is developed. During a field-based analysis, environmental suitability for schistosomiasis transmission was assessed on the ground, which is then quantified by a habitat suitability index (HSI) and applied to RS data. This conceptual model of environmental suitability is refined by the development of a hierarchical model approach that statistically links school-based disease prevalence with the ecologically relevant measurements of RS data. The statistical models of schistosomiasis risk are derived from two different algorithms; the Random Forest and the partial least squares regression (PLSR). Scale impact is analysed based on different spatial resolutions of RS data. Furthermore, varying buffer extents are analysed around school-based measurements. Three distinctive sites of Burkina Faso and Côte d’Ivoire are specifically modelled to represent a gradient of ecozones from dry savannah to tropical rainforest including flat and mountainous regions.
The model results reveal the applicability of RS data to spatially delineate and quantitatively evaluate environmental suitability for the transmission of schistosomiasis. In specific, the multi-temporal derivation of water bodies and the assessment of their riparian vegetation coverage based on high-resolution RapidEye and Landsat data proofed relevant. In contrast, elevation data and water surface temperature are constraint in their ability to characterise habitat conditions for disease-related parasites and freshwater snail species. With increasing buffer extent observed around the school location, the performance of statistical models increases, improving the prediction of transmission risk. The most important RS variables identified to model schistosomiasis risk are the measure of distance to water bodies, topographic variables, and land surface temperature (LST). However, each ecological region requires a different set of RS variables to optimise the modelling of schistosomiasis risk. A key result of the hierarchical model approach is its superior performance to explain the spatial risk of schistosomiasis.
Overall, this study stresses the key importance of considering the ecological and spatial context for disease risk profiling and demonstrates the potential of RS data. The methodological approach of this study contributes substantially to provide more accurate and relevant geoinformation, which supports an efficient planning and decision-making within the public health sector.
Purpose – The purpose of this dissertation is to reveal the status quo of development of the grocery retailers’ internationalization process in China as well as to model future trends, opportunities and challenges within a very competitive market. Using several, geographically distant cities as case studies, this paper focuses on the development and outlook of different store formats, along with the development of competition in this respect by explicitly treating China not as a single market. The study thereby analyses historical and geographical diffusion in regard to store formats. The impacts of the main factors of change are discussed.
Design/methodology/approach – The dissertation reviews extensively the literature of grocery retail internationalization with special focus on China. In addition, it draws on primary research in the form of a wide range of expert interviews. As China´s ‘supermarket revolution’ is underway, an understanding of the local and foreign competition and the development of different store formats within different regions of China as well as their prospects, will be crucial to companies expanding into this area.
Findings – The study explains how grocery retailers have already entered the Chinese market with different store formats and how competition has and will further develop. In addition, the study reveals challenges and obstacles in regard to future market strategies, especially in regard to store formats and geographical regions.
Research limitations/implications – The study reveals the current landscape of the Chinese grocery retailing market and emphasizes important strategic pillars, modelling future implications and challenges for food retailers operating in China. Because China is a vast country this dissertation forms only a small part of the geographical evolution process in regard to store formats and competition.
Practical implications – Explores current understanding of the internationalization process in China by considering different format choices. Supplementary, the dissertation proposes an outlook of competition enlargement, prospects of format development and therewith strategic implications within different regions as well as a future research agenda.
Originality / value – Contributes to the understanding of the Chinese grocery retailing market. Furthermore, it is among the first to critically explore possible future developments in regard to store formats and competition within a geographical context in China
The glaciers in Norway exert a strong influence on Norwegian economy and society. Unlike many glaciers elsewhere and despite ongoing climate change and warming, many of them showed renewed advances and positive net mass changes in the 1980's and 1990's, followed by rapid retreats and mass losses since 2000. This difference in behaviour may be attributed to differences and shifts in the glaciological regime - the differences in the magnitude of impacts of climatic and non-climatic geographical factors on the glacier mass.
This study investigates the influence of various atmospheric variables on mass balance changes of a selection of glaciers in Norway by means of Pearson correlation analyses and cross-validated stepwise multiple regression analyses. The analyses are carried out for three time periods (1949-2008, 1949-1988, 1989-2008) separately in order to take into consideration the possible shift in the glaciological regime in the 1980's. The atmospheric variables are constructed from ERA40 and NCEP/NCAR re-analysis datasets and include regional means of seasonal air temperature and precipitation rates and atmospheric circulation indices. The multiple regression models trained in these time periods are then applied to predictors reconstructed from the CMIP3 climate model dataset to generate an estimate for mass changes from the year 1950 to 2100. The temporal overlap of estimates and observations is used for calibration. Finally, observed atmospheric states in seasons that are characterised by a particularly positive or negative mass balance are categorised into time periods of modelled climate by the application of a Bayesian classification procedure.
The strongest influence on winter mass balance is exerted by different indices of the North Atlantic Oscillation (NAO), Northern Annular Mode (NAM) and precipitation. The correlation coefficients and explained variances determined from the multiple regression analyses reveal an East-West gradient, suggesting a weaker influence of the NAO and NAM on glaciers underlying a more continental regime. The highest correlation coefficients and explained variances were obtained for the 1989-2008 time period, which might be due to a strong and predominantly positive phase of the NAO. Multi-model ensemble means of the estimates show a mass loss for all three eastern glaciers, while the estimates for the more maritime glaciers are ambivalent. In general, the estimates show a greater sensitivity to the training time period than to the greenhouse gas emission scenarios according to which the climates were simulated. The average net mass change by the end of 2100 is negative for all glaciers except for the northern Engabreen. For many glaciers, the Bayesian classification of observed atmospheric states into time periods of modelled climate reveals a decrease in probability of atmospheric states favouring extremes in winter, and an increase in probability of atmospheric states favouring extreme mass loss in summer for the distant future (2071-2100). This pattern of probabilities for the ablation season is most pronounced for glaciers underlying a continental and intermediate regime.
The ecosystem of the high northern latitudes is affected by the recently changing environmental conditions. The Arctic has undergone a significant climatic change over the last decades. The land coverage is changing and a phenological response to the warming is apparent. Remotely sensed data can assist the monitoring and quantification of these changes. The remote sensing of the Arctic was predominantly carried out by the usage of optical sensors but these encounter problems in the Arctic environment, e.g. the frequent cloud cover or the solar geometry. In contrast, the imaging of Synthetic Aperture Radar is not affected by the cloud cover and the acquisition of radar imagery is independent of the solar illumination. The objective of this work was to explore how polarimetric Synthetic Aperture Radar (PolSAR) data of TerraSAR-X, TanDEM-X, Radarsat-2 and ALOS PALSAR and interferometric-derived digital elevation model data of the TanDEM-X Mission can contribute to collect meaningful information on the actual state of the Arctic Environment. The study was conducted for Canadian sites of the Mackenzie Delta Region and Banks Island and in situ reference data were available for the assessment. The up-to-date analysis of the PolSAR data made the application of the Non-Local Means filtering and of the decomposition of co-polarized data necessary.
The Non-Local Means filter showed a high capability to preserve the image values, to keep the edges and to reduce the speckle. This supported not only the suitability for the interpretation but also for the classification. The classification accuracies of Non-Local Means filtered data were in average +10% higher compared to unfiltered images. The correlation of the co- and quad-polarized decomposition features was high for classes with distinct surface or double bounce scattering and a usage of the co-polarized data is beneficial for regions of natural land coverage and for low vegetation formations with little volume scattering. The evaluation further revealed that the X- and C-Band were most sensitive to the generalized land cover classes. It was found that the X-Band data were sensitive to low vegetation formations with low shrub density, the C-Band data were sensitive to the shrub density and the shrub dominated tundra. In contrast, the L-Band data were less sensitive to the land cover. Among the different dual-polarized data the HH/VV-polarized data were identified to be most meaningful for the characterization and classification, followed by the HH/HV-polarized and the VV/VH-polarized data. The quad-polarized data showed highest sensitivity to the land cover but differences to the co-polarized data were small. The accuracy assessment showed that spectral information was required for accurate land cover classification. The best results were obtained when spectral and radar information was combined. The benefit of including radar data in the classification was up to +15% accuracy and most significant for the classes wetland and sparse vegetated tundra. The best classifications were realized with quad-polarized C-Band and multispectral data and with co-polarized X-Band and multispectral data. The overall accuracy was up to 80% for unsupervised and up to 90% for supervised classifications. The results indicated that the shortwave co-polarized data show promise for the classification of tundra land cover since the polarimetric information is sensitive to low vegetation and the wetlands. Furthermore, co-polarized data provide a higher spatial resolution than the quad-polarized data.
The analysis of the intermediate digital elevation model data of the TanDEM-X showed a high potential for the characterization of the surface morphology. The basic and relative topographic features were shown to be of high relevance for the quantification of the surface morphology and an area-wide application is feasible. In addition, these data were of value for the classification and delineation of landforms. Such classifications will assist the delineation of geomorphological units and have potential to identify locations of actual and future morphologic activity.
Agriculture is mankind’s primary source of food production and plays the key role for cereal supply to humanity. One of the future challenges will be to feed a constantly growing population, which is expected to reach more than nine billion by 2050. The potential to expand cropland is limited, and enhancing agricultural production efficiency is one important means to meet the future food demand. Hence, there is an increasing demand for dependable, accurate and comprehensive agricultural intelligence on crop production. The value of satellite earth observation (EO) data for agricultural monitoring is well recognized. One fundamental requirement for agricultural monitoring is routinely updated information on crop acreage and the spatial distribution of crops. With the technical advancement of satellite sensor systems, imagery with higher temporal and finer spatial resolution became available. The classification of such multi-temporal data sets is an effective and accurate means to produce crop maps, but methods must be developed that can handle such large and complex data sets. Furthermore, to properly use satellite EO for agricultural production monitoring a high temporal revisit frequency over vast geographic areas is often necessary. However, this often limits the spatial resolution that can be used. The challenge of discriminating pixels that correspond to a particular crop type, a prerequisite for crop specific agricultural monitoring, remains daunting when the signal encoded in pixels stems from several land uses (mixed pixels), e.g. over heterogeneous landscapes where individual fields are often smaller than individual pixels.
The main purposes of the presented study were (i) to assess the influence of input dimensionality and feature selection on classification accuracy and uncertainty in object-based crop classification, (ii) to evaluate if combining classifier algorithms can improve the quality of crop maps (e.g. classification accuracy), (iii) to assess the spatial resolution requirements for crop identification via image classification.
Reporting on the map quality is traditionally done with measures that stem from the confusion matrix based on the hard classification result. Yet, these measures do not consider the spatial variation of errors in maps. Measures of classification uncertainty can be used for this purpose, but they have attained only little attention in remote sensing studies. Classifier algorithms like the support vector machine (SVM) can estimate class memberships (the so called soft output) for each classified pixel or object. Based on these estimations, measures of classification uncertainty can be calculated, but it has not been analysed in detail, yet, if these are reliable in predicting the spatial distribution of errors in maps. In this study, SVM was applied for the classification of agricultural crops in irrigated landscapes in Middle Asia at the object-level. Five different categories of features were calculated from RapidEye time series data as classification input. The reliability of classification uncertainty measures like entropy, derived from the soft output of SVM, with regard to predicting the spatial distribution of error was evaluated. Further, the impact of the type and dimensionality of the input data on classification uncertainty was analysed. The results revealed that SMVs applied to the five feature categories separately performed different in classifying different types of crops. Incorporating all five categories of features by concatenating them into one stacked vector did not lead to an increase in accuracy, and partly reduced the model performance most obviously because of the Hughes phenomena. Yet, applying the random forest (RF) algorithm to select a subset of features led to an increase of classification accuracy of the SVM. The feature group with red edge-based indices was the most important for general crop classification, and the red edge NDVI had an outstanding importance for classifying crops. Two measures of uncertainty were calculated based on the soft output from SVM: maximum a-posteriori probability and alpha quadratic entropy. Irrespective of the measure used, the results indicate a decline in classification uncertainty when a dimensionality reduction was performed. The two uncertainty measures were found to be reliable indicators to predict errors in maps. Correctly classified test cases were associated with low uncertainty, whilst incorrectly test cases tended to be associated with higher uncertainty.
The issue of combining the results of different classifier algorithms in order to increase classification accuracy was addressed. First, the SVM was compared with two other non-parametric classifier algorithms: multilayer perceptron neural network (MLP) and RF. Despite their comparatively high classification performance, each of the tested classifier algorithms tended to make errors in different parts of the input space, e.g. performed different in classifying crops. Hence, a combination of the complementary outputs was envisaged. To this end, a classifier combination scheme was proposed, which is based on existing algebraic operators. It combines the outputs of different classifier algorithms at the per-case (e.g. pixel or object) basis. The per-case class membership estimations of each classifier algorithm were compared, and the reliability of each classifier algorithm with respect to classifying a specific crop class was assessed based on the confusion matrix. In doing so, less reliable classifier algorithms were excluded at the per-class basis before the final combination. Emphasis was put on evaluating the selected classification algorithms under limiting conditions by applying them to small input datasets and to reduced training sample sets, respectively. Further, the applicability to datasets from another year was demonstrated to assess temporal transferability. Although the single classifier algorithms performed well in all test sites, the classifier combination scheme provided consistently higher classification accuracies over all test sites and in different years, respectively. This makes this approach distinct from the single classifier algorithms, which performed different and showed a higher variability in class-wise accuracies. Further, the proposed classifier combination scheme performed better when using small training set sizes or when applied to small input datasets, respectively.
A framework was proposed to quantitatively define pixel size requirements for crop identification via image classification. That framework is based on simulating how agricultural landscapes, and more specifically the fields covered by one crop of interest, are seen by instruments with increasingly coarser resolving power. The concept of crop specific pixel purity, defined as the degree of homogeneity of the signal encoded in a pixel with respect to the target crop type, is used to analyse how mixed the pixels can be (as they become coarser) without undermining their capacity to describe the desired surface properties (e.g. to distinguish crop classes via supervised or unsupervised image classification). This tool can be modulated using different parameterizations to explore trade-offs between pixel size and pixel purity when addressing the question of crop identification. Inputs to the experiments were eight multi-temporal images from the RapidEye sensor. Simulated pixel sizes ranged from 13 m to 747.5 m, in increments of 6.5 m. Constraining parameters for crop identification were defined by setting thresholds for classification accuracy and uncertainty. Results over irrigated agricultural landscapes in Middle Asia demonstrate that the task of finding the optimum pixel size did not have a “one-size-fits-all” solution. The resulting values for pixel size and purity that were suitable for crop identification proved to be specific to a given landscape, and for each crop they differed across different landscapes. Over the same time series, different crops were not identifiable simultaneously in the season and these requirements further changed over the years, reflecting the different agro-ecological conditions the investigated crops were growing in. Results further indicate that map quality (e.g. classification accuracy) was not homogeneously distributed in a landscape, but that it depended on the spatial structures and the pixel size, respectively. The proposed framework is generic and can be applied to any agricultural landscape, thereby potentially serving to guide recommendations for designing dedicated EO missions that can satisfy the requirements in terms of pixel size to identify and discriminate crop types.
Regarding the operationalization of EO-based techniques for agricultural monitoring and its application to a broader range of agricultural landscapes, it can be noted that, despite the high performance of existing methods (e.g. classifier algorithms), transferability and stability of such methods remain one important research issue. This means that methods developed and tested in one place might not necessarily be portable to another place or over several years, respectively. Specifically in Middle Asia, which was selected as study region in this thesis, classifier combination makes sense due to its easy implementation and because it enhanced classification accuracy for classes with insufficient training samples. This observation makes it interesting for operational contexts and when field reference data availability is limited. Similar to the transferability of methods, the application of only one certain kind of EO data (e.g. with one specific pixel size) over different landscapes needs to be revisited and the synergistic use of multi-scale data, e.g. combining remote sensing imagery of both fine and coarse spatial resolution, should be fostered. The necessity to predict and control the effects of spatial and temporal scale on crop classification is recognized here as a major goal to achieve in EO-based agricultural monitoring.
Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 %. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 % for cotton and 30.4 % for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 % to 23.8 % for cotton and from -0.4 % to -19.4 % for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.
Rapid population growth in West Africa has led to expansion in croplands due to the need to grow more food to meet the rising food demand of the burgeoning population. These expansions negatively impact the sub-region's ecosystem, with implications for water and soil quality, biodiversity and climate. In order to appropriately monitor the changes in croplands and assess its impact on the ecosystem and other environmental processes, accurate and up-to-date information on agricultural land use is required. But agricultural land use mapping (i.e. mapping the spatial distribution of crops and croplands) in West Africa has been challenging due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. This study, therefore, investigated the possibilities of improving agricultural land use mapping by utilizing optical satellite images with higher spatial and temporal resolution as well as images from Synthetic Aperture Radar (SAR) systems which are near-independent of weather conditions. The study was conducted at both watershed and regional scales.
At watershed scale, classification of different crop types in three watersheds in Ghana, Burkina Faso and Benin was conducted using multi-temporal: (1) only optical images (RapidEye) and (2) optical plus dual polarimetric (VV/VH) SAR images (TerraSAR-X). In addition, inter-annual or short term (2-3 years) changes in cropland area in the past ten years were investigated using historical Landsat images. Results obtained indicate that the use of only optical images to map different crop types in West Africa can achieve moderate classification accuracies (57% to 71%). Overlaps between the cropping calendars of most crops types and certain inter-croppings pose a challenge to optical images in achieving an adequate separation between those crop classes. Integration of SAR images, however, can improve classification accuracies by between 8 and 15%, depending on the number of available images and their acquisition dates. The sensitivity of SAR systems to different crop canopy architectures and land surface characteristics improved the separation between certain crop types. The VV polarization of TerraSAR-X was found to better discrimination between crop types than the VH. Images acquired between August and October were found to be very useful for crop mapping in the sub-region due to structural differences in some crop types during this period.
At the regional scale, inter-annual or short term changes in cropland area in the Sudanian Savanna agro-ecological zone in West Africa were assessed by upscaling historical cropland information derived at the watershed scale (using Landsat imagery) unto a coarse spatial resolution, but geographically large, satellite imagery (MODIS) using regression based modeling. The possibility of using such regional scale cropland information to improve government-derived agricultural statistics was investigated by comparing extracted cropland area from the fractional cover maps with district-level agricultural statistics from Ghana The accuracy of the fractional cover maps (MAE between 14.2% and 19.1%) indicate that the heterogeneous agricultural landscape of West Africa can be suitably represented at the regional or continental scales by estimating fractional cropland cover on low resolution Analysis of the results revealed that cropland area in the Sudanian Savanna zone has experienced inter-annual or short term fluctuations in the past ten years due to a variety of factors including climate factors (e.g. floods and droughts), declining soil fertility, population increases and agricultural policies such as fertilizer subsidies. Comparison of extracted cropland area from the fractional cover maps with government's agricultural statistics (MoFA) for seventeen districts (second administrative units) in Ghana revealed high inconsistencies in the government statistics, and highlighted the potential of satellite derived cropland information at regional scales to improve national/sub-national agricultural statistics in West Africa.
The results obtained in this study is promising for West Africa, considering the recent launch of optical (Landsat 8) and SAR sensors (Sentinel-1) that will provide free data for crop mapping in the sub-region. This will improve chances of obtaining adequate satellite images acquired during the cropping season for agricultural land use mapping and bolster opportunities of operationalizing agricultural land use mapping in West Africa. This can benefit a wide range of biophysical and economic models and improve decision making based on their results.
Information on the state of the terrestrial vegetation cover is important for several ecological, economical, and planning issues. In this regard, vegetation properties such as the type, vitality, or density can be described by means of continuous biophysical parameters. One of these parameters is the leaf area index (LAI), which is defined as half the total leaf area per unit ground surface area. As leaves constitute the interface between the biosphere and the atmosphere, the LAI is used to model exchange processes between plants and their environment. However, to account for the variability of ecosystems, spatially and temporally explicit information on LAI is needed both for monitoring and modeling applications.
Remote sensing aims at providing such information. LAI is commonly derived from remote sensing data by empirical-statistical or physical models. In the first approach, an empirical relationship between LAI measured in situ and the corresponding canopy spectral signature is established. Although this method achieves accurate LAI estimates, these relationships are only valid for the place and time at which the field data were sampled, which hampers automated LAI derivation. The physical approach uses a radiation transfer model to simulate canopy reflectance as a function of the scene’s geometry and of leaf and canopy parameters, from which LAI is derived through model inversion based on remote sensing data. However, this model inversion is not stable, as it is an under-determined and ill-posed problem.
Until now, LAI research focused either on the use of coarse resolution remote sensing data for global applications, or on LAI modeling over a confined area, mostly in forest and crop ecosystems, using medium to high spatial resolution data. This is why to date no study is available in which high spatial resolution data are used for LAI mapping in a heterogeneous, natural landscape such as alpine grasslands, although a growing amount of high spatial and temporal resolution remote sensing data would allow for an improved environmental monitoring. Therefore, issues related to model parameterization and inversion regularization techniques improving its stability have not yet been investigated for this ecosystem.
This research gap was taken up by this thesis, in which the potential of high spatial resolution remote sensing data for grassland LAI estimation based on statistical and radiation transfer modeling is analyzed, and the achieved accuracy and robustness of the two approaches is compared. The objectives were an ecosystem-adapted radiation transfer model set-up and an optimized LAI derivation in mountainous grassland areas. Multi-temporal LAI in situ measurements as well as time series of RapidEye data from 2011 and 2012 over the catchment of the River Ammer in the Bavarian alpine upland were used. In order to obtain accurate in situ data, a comparison of the LAI derivation algorithms implemented in the LAI-2000 PCA instrument with destructively measured LAI was performed first. For optimizing the empirical-statistical approach, it was then analyzed how the selection of vegetation indices and regression models impacts LAI modeling, and how well these models can be transferred to other dates. It was shown that LAI can be derived
with a mean accuracy of 80 % using contemporaneous field data, but that the accuracy decreases to on average 51 % when using these models on remote sensing data from other dates. The combined use of several data sets to create a regression which is used for LAI derivation at different points in time increased the LAI estimation accuracy to on average 65 %. Thus, reduced field measurement labor comes at the cost of LAI error rates being increased by 10 - 30 % as long as at least two campaigns are conducted. Further, it was shown that the use of RapidEye’s red edge channel improves the LAI derivation by on average 5.4 %.
With regard to physical LAI modeling, special interest lay in assessing the accuracy improvements that can be achieved through model set-up and inversion regularization techniques. First, a global sensitivity analysis was applied to the radiation transfer model in order to identify the most important model parameters and most sensitive spectral features. After model parameterization, several inversion regularizations, namely the use of a multiple sample solution, the additional use of vegetation indices, and the addition of noise, were analyzed. Further, an approach to include the local scene’s geometry in the retrieval process was introduced to account for the mountainous topography. LAI modeling accuracies of in average 70 % were achieved using the best combination of regularization techniques, which is in the upper range of accuracies that were achieved in the few existing other grassland studies based on in situ or air-borne measured hyperspectral data. Finally, further physically derived vegetation parameters and inversion uncertainty measures were evaluated in detail to identify challenging modeling conditions, which was mostly neglected in other studies. An increased modeling uncertainty for extremely high and low LAI values was observed. This indicates an insufficiently wide model parameterization and a canopy deviation from model assumptions on some fields. Further, the LAI modeling accuracies varied strongly between the different scenes. From this observation it can be deduced that the radiometric quality of the remote sensing data, which might be reduced by atmospheric effects or unexpected surface reflectances, exerts a high influence on the LAI modeling accuracy.
The major findings of the comparison between the empirical-statistical and physical LAI modeling approaches are the higher accuracies achieved by the empirical-statistical approach as long as contemporaneous field data are available, and the computationally efficiency of the statistical approach. However, when no or temporally unfitting in situ measurements are available, the physical approach achieves comparable or even higher accuracies. Furthermore, radiation transfer modeling enables the derivation of other leaf and canopy variables useful for ecological monitoring and modeling applications, as well as of pixel-wise uncertainty measures indicating the robustness and reliability of the model inversion and LAI derivation procedure. The established look-up tables can be used for further LAI derivation in Central European grassland also in other years.
The use of high spatial resolution remote sensing data for LAI derivation enables a reliable land cover classification and thus a reduced LAI mapping error due to misclassifications. Furthermore, the RapidEye pixels being smaller than individual fields allow for a radiation transfer model inversion over homogeneous canopies in most cases, as canopy gaps or field parcels can be clearly distinguished. However, in case of unexpected local surface conditions such as blooming, litter, or canopy gaps, high spatial resolution data show corresponding strong deviations in reflectance values and hence LAI estimation, which would be reduced using coarser resolution data through the balancing effect of the surrounding surface reflectances. An optimal pixel size with regard to modeling accuracy hence depends on the canopy and landscape structure. Furthermore, a reduced spatial resolution would enable a considerable acceleration of the LAI map derivation.
This illustration of the potential of RapidEye data and of the challenges associated to LAI derivation in heterogeneous grassland areas contributes to the development of robust LAI estimation procedures based on new and upcoming, spatially and temporally high resolution remote sensing imagery such as Landsat 8 and Sentinel-2.
Nature-based tourism and ecotourism experienced a dynamic development over the past decade. While originally often described as specialized post-Fordist niche markets for ecologically aware and affluent target groups, in many regions they are nowadays characterized by a heterogeneous structure and the presence of a wide product range, from individual travels to package tours.
The present dissertation analyzes the structure and economic importance of tourism in two highly frequented protected areas in middle income countries, the Sian Ka’an Biosphere Reserve (SKBR) in Mexico and the Souss-Massa National Park (SMNP) in Morocco. Both areas are situated in close proximity to the most important package tour destinations Cancún (Mexico) and Agadir (Morocco) and are subject to high touristic use and development pressure. So far, the planning of a more sustainable tourism development is hampered by the lack of reliable data.
Based on demand-side surveys and income multipliers calculated with the help of regionalized input-output models, the visitor structure and economic impact of tourism in both protected areas are described. With regional income effects of approximately 1 million USD (SKBR) and approximately 1.9 million USD (SMNP), and resulting income equivalents of 1,348 and 5,218 persons, both the SKBR and the SMNP play an important—and often undervalued—role for the regional economies in underdeveloped rural peripheral regions of the countries.
Detailed analyses of the visitor structures show marked differences with regard to criteria such as travel organization, nature/protected area affinity and expenditures. With regard to planning and marketing of nature-based tourism, protected area managers and political decision-takers are advised to focus on ecologically and economically attractive visitor groups. Based on the results of the two case studies as well as existing tourism typologies from the literature, a classification scheme is presented that may be used for a more target-oriented development and marketing of nature-based tourism products.
The Mediterranean area reveals a strong vulnerability to future climate change due to a high exposure to projected impacts and a low capacity for adaptation highlighting the need for robust regional or local climate change projections, especially for extreme events strongly affecting the Mediterranean environment. The prevailing study investigates two major topics of the Mediterranean climate variability: the analysis of dynamical downscaling of present-day and future temperature and precipitation means and extremes from global to regional scale and the comprehensive investigation of temperature and rainfall extremes including the estimation of uncertainties and the comparison of different statistical methods for precipitation extremes. For these investigations, several observational datasets of CRU, E-OBS and original stations are used as well as ensemble simulations of the regional climate model REMO driven by the coupled global general circulation model ECHAM5/MPI-OM and applying future greenhouse gas (GHG) emission and land degradation scenarios.
Understanding the mechanisms of fragmentation within silicate melts is of great interest not only for material science, but also for volcanology, particularly regarding molten fuel coolant-interactions (MFCIs). Therefore edge-on hammer impact experiments (HIEs) have been carried out in order to analyze the fracture dynamics in well defined targets by applying a Cranz-Schardin highspeed camera technique. This thesis presents the corresponding results and provides a thorough insight into the dynamics of fragmentation, particularly focussing on the processes of energy dissipation. In HIEs two main classes of cracks can be identified, characterized by completely different fracture mechanisms: Shock wave induced “damage cracks” and “normal cracks”, which are exclusively caused by shear-stresses. This dual fracture situation is taken into account by introducing a new concept, according to which the crack class-specific fracture energies are linearly correlated with the corresponding fracture areas. The respective proportionality constants - denoted “fracture surface energy densities” (FSEDs) - have been quantified for all studied targets under various constraints. By analyzing the corresponding high speed image sequences and introducing useful dynamic parameters it has been possible to specify and describe in detail the evolution of fractures and, moreover, to quantify the energy dissipation rates during the fragmentation. Additionally, comprehensive multivariate statistical analyses have been carried out which have revealed general dependencies of all relevant fracture parameters as well as characteristics of the resulting particles. As a result, an important principle of fracture dynamics has been found, referred to as the “local anisotropy effect”: According to this principle, the fracture dynamics in a material is significantly affected by the location of directed stresses. High local stress gradients cause a more stable crack propagation and consequently a reduction of the energy dissipation rates. As a final step, this thesis focusses on the volcanological conclusions which can be drawn on the basis of the presented HIE results. Therefore fragments stemming from HIEs have been compared with natural and experimental volcanic ash particles of basaltic Grimsvötn and rhyolitic Tepexitl melts. The results of these comparative particle analyses substantiate HIEs to be a very suitable method for reproducing the MFCI loading conditions in silicate melts and prove the FSED concept to be a model which is well transferable to volcanic fragmentation processes.
U.S. and German Approaches to Regulating Retail Development: Urban Planning Tools and Local Policies
(2012)
This dissertation examines retail development regulation in the U.S. and in Germany, comparing the various urban planning tools and policies in use by municipal governments. These similarities and differences are explored through research into three case study cities in each country, with special attention paid to how these governments regulate large-scale or "big box" retail.
Climate change assessment in Southeast Asia and implications for agricultural production in Vietnam
(2011)
For many years, the study of climatic changes and variations has become the main objective of climatic research, as has been appreciated in the IPCC's reports and several publications regarding climatic evolution on different space-time scales. Since the 80's, many research groups have generated the extensive database from which the analysis of temperature, precipitation and other climatic parameters has been performed on a global scale (Jones et al., 1986; Hansen and Lebedeff, 1987, 1988; Vinnikov et al., 1987, 1990). The most important result of these research projects is the evidence of global warming during the 20th century, especially in the last two decades. However, numerous challenges still exist about the structure and dimension of the climatic change on a considerable scale. Therefore, it is necessary to carry out studies on a local and regional scale that allow for a more precise evaluation of the global warming phenomenon. A statistical analysis approach was developed to identify systematic differences between large-scale climatic variable from the General Circulation Models (GCM), NCEP, CRU re-analysis data set and climatic parameters (temperature and precipitation data). Models are able to satisfactorily reproduce the spatial patterns of the regional temperature and precipitation field. The response of the climate system to various emission scenario simulated by the GCM was used to analyze and predict the local climate change. The main objective of this study is to analysis the time evolution of the annual and seasonal temperature and precipitation during the 21st century and in order to contribute to our knowledge of temperature and precipitation trends over the century on a regional scale, not only in Southeast Asia but also in Vietnam; the study focuses to develop a dynamical – statistical model describing the relationship between the major climate variation and agricultural production in Vietnam. This study will be an important contribution to the present-day assessment of climate change impacts in the low latitudes. Regional scenarios of climate change, including both rainfall and mean temperature were then used to assess the impact of climate change on crop production in the region in order to evaluate the vulnerability of the system to global warming. Climate change has adverse impacts on the socio - economic development of all nations. But the degree of the impact will vary across nations. It is expected that changes in the earth's climate will impact on developing countries like Vietnam, in particular, hardest because their economies are strongly dependent on crude forms of natural resources and their economic structure is less flexible to adjust to such drastic changes. In Chapter 1: Introduction and background I describe in general terms climate, climate change, climate change model with benefits and problems. Chapter 2: methodology discusses the methods including interpolation, validation, clustering, correlation and regression which were applied in the study. Chapter 3 and chapter 4 describe the database and study area. The most important is chapter 5 Results. The last is chapter 6 Conclusion and outlook followed by the reference list and an appendix.
The present study concerned mainly on the source, facies, and sedimentary environments of the Middle to Upper Jurassic strata in the Kerman and Tabas areas, east-central Iran. The composition of sandstones, and heavy mineral analysis point to pre-existing sedimentary, low, middle to upper rank metamorphic, and plutonic rocks of the Kalmard, Posht-e-Badam, Bayazeh, and Zarand-Kerman areas as the source rocks. According to the diagram of WELTJE et al. (1998), most samples from the Middle-Upper Jurassic rocks suggest a moderate to high elevation of the source area, and indicate a semi-arid and mediterranean to sub-humid climate. In the Qt-F-L ternary diagrams of DICKINSON et al. (1983), most point counting data from the Lower Siliciclastic Member and the top of the Hojedk Formation plot in the recycled orogen (Quartzose recycled) area of the diagram. The sandstones in this area can be interpreted as being derived from the Mid-Cimmerian Movements. Sixteen different types of siliciclastic-carbonate, and evaporatic sedimentary environments have been recognized. Thirty-nine macroinvertebrate taxa have been identified. Ten ichnotaxa have been taxonomically described from the Middle to Upper Jurassic rocks. Quite likely, before rotation of CEIM which were associated with counterclockwise block-rotation, equivalent rocks of the Bidou Formation occurred along the tectonic zone between the Yazd and the Tabas blocks (probably during the Middle Jurassic to Lower Cretaceous). However, from the Cretaceous onwards, most of the Bidou Formation has been removed by a combination of strike-slip and reverse movements of the Kashmar-Kerman tectonic zone. Roughly, these block-rotation movements occurred after the Cretaceous. During the Middle to Upper Jurassic, the tectonic activities were vertical movements producing the sedimentary pattern in the CEIM.
Taxonomy and palaeoecology of the Cenomanian-Turonian macro-invertebrate from eastern Sinai, Egypt
(2010)
The present study concerened with taxonomy and palaeoecology of the Cenomanian-Turonian macrobenthic fauna which includes bivalves, gastropods, echinoids, and coral. In addtion, cephalopods are also taken in consideration. 144 taxa are identified and systematically described. Palaeoecological and taphonomic anylsis of the statistically sampled macrobenthos are also discussed. The biostratigraphic sequences along the Cenomanian-Turonian rocks were carried out on the basis of ammonites and other macrobenthic fauna such as corals and bivalves. In order to reconstruct benthic association, 41 statistically sampled were subjected to cluster ananlysis by using Past Programm (Hammer et al., 2001). 10 association and three assemblages were described in order to reconstruct the different depositional enviroments.
Mapping Bushfire Distribution and Burn Severity in West Africa Using Remote Sensing Observations
(2010)
Fire has long been considered to be the main ecological factor explaining the origin and maintenance of West African savannas. It has a very high occurrence in these savannas due to high human pressure caused by strong demographic growth and, concomitantly, is used to transform natural savannas into farmland and is also used as a provider of energy. This study was carried out with the support of the BIOTA project funded by the German ministry for Research and Education. The objective of this study is to establish the spatial and temporal distribution of bushfires during a long observation period from 2000 to 2009 as well as to assess fire impact on vegetation through mapping of the burn severity; based on remote sensing and field data collections. Remote sensing was used for this study because of the advantages that it offers in collecting data for long time periods and on different scales. In this case, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument at 1km resolution is used to assess active fires, and understand the seasonality of fire, its occurrence and its frequency within the vegetation types on a regional scale. Landsat ETM+ imagery at 30 m and field data collections were used to define the characteristics of burn severity related to the biomass loss on a local scale. At a regional scale, the occurrence of fires and rainfall per month correlated very well (R2 = 0.951, r = -0.878, P < 0.01), which shows that the lower the amount of rainfall, the higher the fire occurrence and vice versa. In the dry season, four fire seasons were determined on a regional scale, namely very early fires, which announce the beginning of the fires, early and late fires making up the peak of fire in December/January and very late fires showing the end of the fire season and the beginning of the rainy season. Considerable fire activity was shown to take place in the vegetation zones between the Forest and the Sahel areas. Within these zones, parts of the Sudano-Guinean and the Guinean zones showed a high pixel frequency, i.e. fires occurred in the same place in many years. This high pixel frequency was also found in most protected areas in these zones. As to the kinds of land cover affected by fire, the highest fire occurrence is observed within the Deciduous woodlands and Deciduous shrublands. Concerning the burn severity, which was observed at a local scale, field data correlated closely with the ΔNBR derived from Landsat scenes of Pendjari National Park (R2 = 0.76). The correlation coefficient according to Pearson is r = 0.84 and according to Spearman-Rho, the correlation coefficient is r = 0.86. Very low and low burn severity (with ΔNBR value from 0 to 0.40) affected the vegetation weakly (0-35 percent of biomass loss) whereas moderate and high burn severity greatly affected the vegetation, leading to up to 100 percent of biomass loss, with the ΔNBR value ranging from 0.41 to 0.99. It can be seen from these results that remotely sensed images offer a tool to determine the fire distribution over large regions in savannas and that the Normalised Burn Ratio index can be applied to West Africa savannas. The outcomes of this thesis will hopefully contribute to understanding and, eventually, improving fire regimes in West Africa and their response to climate change and changes in vegetation diversity.
This work presents a new method to measure model independent viscosities of inhomogeneous materials at high temperatures. Many mechanisms driving volcanic eruptions are strongly influenced by the viscous properties of the participating materials. Since an eruption takes place at temperatures at which these materials (predominantly silicate melts) are not completely molten, typically inhomogeneities, like e.g. equilibrium and non-equilibrium crystals, are present in the system. In order to incorporate such inhomogeneities into objective material parameters the viscosity measurement is based on a rotational viscometer in a wide gap Couette setup. The gap size between the two concentric cylinders was designed as large as possible in order to account for the inhomogeneities. The emerging difficulties concerning the model independent data reduction from measured values to viscosities are solved using an appropriate interpolation scheme. The method was applied to a material representative for the majority of volcanic eruptions on earth: a typical continental basaltic rock (Billstein/Rhön/Germany). The measured viscosities show a strong shear rate dependency, which surprises, because basaltic melt has been, until now, assumed to behave as a Newtonian fluid. Since a non-Newtonian material shows a very different relaxation behavior in the Couette motion compared to a Newtonian one (which, ultimately, does not show any), and a strong relaxation signal was recorded during viscosity measurements, the equations of Couette motion were investigated. The time dependent stress distribution in a material due to a quasi step-like velocity change at the inner Couette radius (i.e. the spindle) was considered. The results show that a material combining a linear shear modulus and a Newtonian viscosity -- a Maxwell material -- cannot quantify the relaxation behavior. This could be considered as a hint, that the widely used Maxwell relaxation times cannot be applied as a 1:1 mapping from microscopic considerations to macroscopic situations.
The urban micro climate has been increasingly recognised as an important aspect for urban planning. Therefore, urban planners need reliable information on the micro climatic characteristics of the urban environment. A suitable spatial scale and large spatial coverage are important requirements for such information. This thesis presents a conceptual framework for the use of airborne hyperspectral data to support urban micro climate characterisation, taking into account the information needs of urban planning. The potential of hyperspectral remote sensing in characterising the micro climate is demonstrated and evaluated by applying HyMap airborne hyperspectral and height data to a case study of the German city of Munich. The developed conceptual framework consists of three parts. The first is concerned with the capabilities of airborne hyperspectral remote sensing to map physical urban characteristics. The high spatial resolution of the sensor allows to separate the relatively small urban objects. The high spectral resolution enables the identification of the large range of surface materials that are used in an urban area at up to sub-pixel level. The surface materials are representative for the urban objects of which the urban landscape is composed. These spatial urban characteristics strongly influence the urban micro climate. The second part of the conceptual framework provides an approach to use the hyperspectral surface information for the characterisation of the urban micro climate. This can be achieved by integrating the remote sensing material map into a micro climate model. Also spatial indicators were found to provide useful information on the micro climate for urban planners. They are commonly used in urban planning to describe building blocks and are related to several micro climatic parameters such as temperature and humidity. The third part of the conceptual framework addresses the combination and presentation of the derived indicators and simulation results under consideration of the planning requirements. Building blocks and urban structural types were found to be an adequate means to group and present the derived information for micro climate related questions to urban planners. The conceptual framework was successfully applied to a case study in Munich. Airborne hyperspectral HyMap data has been used to derive a material map at sub-pixel level by multiple endmember linear spectral unmixing. This technique was developed by the German Research Centre for Geosciences (GFZ) for applications in Dresden and Potsdam. A priori information on building locations was used to support the separation between spectrally similar materials used both on building roofs and non-built surfaces. In addition, surface albedo and leaf area index are derived from the HyMap data. The sub-pixel material map supported by object height data is then used to derive spatial indicators, such as imperviousness or building density. To provide a more detailed micro climate characterisation at building block level, the surface materials, albedo, leaf area index (LAI) and object height are used as input for simulations with the micro climate model ENVI-met. Concluding, this thesis demonstrated the potential of hyperspectral remote sensing to support urban micro climate characterisation. A detailed mapping of surface materials at sub-pixel level could be performed. This provides valuable, detailed information on a large range of spatial characteristics relevant to the assessment of the urban micro climate. The developed conceptual framework has been proven to be applicable to the case study, providing a means to characterise the urban micro climate. The remote sensing products and subsequent micro climatic information are presented at a suitable spatial scale and in understandable maps and graphics. The use of well-known spatial indicators and the framework of urban structural types can simplify the communication with urban planners on the findings on the micro climate. Further research is needed primarily on the sensitivity of the micro climate model towards the remote sensing based input parameters and on the general relation between climate parameters and spatial indicators by comparison with other cities.
This study presents new petrological results obtained from high-grade metamorphic rocks of the Beit Bridge, Mahalapye and Phikwe Complexes, which constitute the Central Zone of the Limpopo Belt in southern Africa. These results provide detailed information about the prograde and retrograde pressure-temperature (P-T) evolution of the three investigated complexes and, in concert with geochronological data, form the basis for the development of a coherent geodynamic model for the evolution of the Limpopo’s Central Zone. The P-T paths were inferred by the thorough investigation of silica-saturated and silica- undersaturated metapelitic and metabasic rocks, comprising six sillimanite-garnet-cordierite gneisses, four (garnet)-biotite-plagioclase gneisses, two garnet-orthopyroxene-biotite-Kfeldspar-plagioclase gneisses, one garnet- cordierite-orthoamphibole fels, one garnet-biotite amphibolite, and one garnet-clinopyroxene amphibolite. P-T points and P-T evolutions were derived by the application of conventional geothermobarometers, and quantitative phase diagrams in the systems Na2O - CaO - K2O - FeO - MgO - Al2O3 - SiO2 - H2O - TiO2 - O (NCKFMASHTiO), and MnO - TiO2 - Na2O - CaO - K2O - FeO - MgO - Al2O3 - SiO2 - H2O (MnTiNCKFMASH) - using the computer software THERMOCALC and THERIAK-DOMINO. The petrological information, in particular those obtained by comparison between observed and thermodynamically calculated mineral assemblages, zonations and modes, in combination with new and existing geochronological data provide evidence that rocks from the three investigated complexes underwent slightly different P-T evolutions at different times. The samples from the Bulai Pluton area (Beit Bridge Complex) provide evidence for a Neoarchean high-grade metamorphic event at ~2.64 Ga (M2), with peak P-T conditions of ~850°C at 8-9 kbar, and a decompression-cooling path to ~750°C at 5-6 kbar. This metamorphic evolution perhaps took place in a magmatic arc setting. In contrast, samples from the Mahalapye and Phikwe Complex document a Palaeoproterozoic event at ~2.03-2.05 Ga (M3), and were subject to different styles of prograde metamorphism. Metamorphic rocks from the Mahalapye Complex experienced a high-temperature low-pressure (HT-LP) metamorphic overprint, accompanied by the emplacement of voluminous granite bodies between 2.06 and 2.02 Ga, and provide evidence for a slightly prograde decompression from ~650°C/7 kbar to ~800°C/5.5 kbar. In contrast, the metamorphic rocks from the Phikwe Complex provide evidence for a simultaneous pressure and temperature increase from ~600°C/6 kbar to ~750°C/8 kbar, in the absence of significant Palaeoproterozoic magmatism. The HT-LP metamorphic evolution of the Mahalapye Complex is interpreted to be initiated by the underplating of hot mafic melts, either formed in response to SE-subduction during the Kheis-Magondi orogeny, and/or by contemporaneous mantle plume activities related to the formation of the Bushveld Complex. In contrast, the prograde pressure and temperature increase reflected by the rocks from the Phikwe Complex rather reflects successive crustal stacking at ~2.03 Ga. This stacking, which is also reported from many other units throughout the Limpopo Belt, is interpreted to result from the final convergence between the Kaapvaal and Zimbabwe Cratons, perhaps caused by SE-directed compression in response to the Kheis-Magondi orogeny between ~2.06 and 1.90 Ga.
Glacier outlines during the ‘Little Ice Age’ maximum in Jotunheimen were mapped by using remote sensing techniques (vertical aerial photos and satellite imagery), glacier outlines from the 1980s and 2003, a digital terrain model (DTM), geomorphological maps of individual glaciers, and field-GPS measurements. The related inventory data (surface area, minimum and maximum altitude) and several other variables (e.g. slope, range) were calculated automatically by using a geographical information system. The length of the glacier flowline was mapped manually based on the glacier outlines at the maximum of the ‘Little Ice Age’ and the DTM. The glacier data during the maximum of the ‘Little Ice Age’ were compared with the Norwegian glacier inventory of 2003. Based on the glacier inventories during the maximum of the ‘Little Ice Age’, the 1980s and 2003, a simple parameterization after HAEBERLI & HOELZLE (1995) was performed to estimate unmeasured glacier variables, as e.g. surface velocity or mean net mass balance. Input data were composed of surface glacier area, minimum and maximum elevation, and glacier length. The results of the parameterization were compared with the results of previous parameterizations in the European Alps and the Southern Alps of New Zealand (HAEBERLI & HOELZLE 1995; HOELZLE et al. 2007). A relationship between these results of the inventories and of the parameterization and climate and climate changes was made.
The Upper Bajocian-Bathonian Kashafrud Formation is a thick package of siliciclastic sediments that crops out in NE Iran from the southeast, near the Afghanistan border, to north- northwestern areas around the city of Mashhad. The thickness ranges from less than 300 m in a deltaic succession (Kuh-e-Radar) to more than 2500 m in the Maiamay area, but the normal thickness in Ghal-e-Sangi, Kol-e-Malekabad, and Fraizi areas is about 1200-1300 m. It is the fill of an elongated basin, which extended for more than 200 km in NW-SE direction and a width of at least 50 km along the southern margin of the Koppeh Dagh. Prior to this study, little information existed about the sedimentary environments and other characters, especially the geometry of the basin. Exact biostratigraphic data from the top of the Kashafrud Formation were rare. Based on the macrofauna from the lower part of the overlying Chamanbid Formation the upper boundary of the Kashafrud Formation had been attributed to the Late Bathonian and/or Early Callovian, but now the upper limit of the Kashafrud Formation is defined as Late Bathonian in age, based on ammonite biostratigraphy. Except for chapter one, which deals with the introduction and related sub-titles, in the following chapters, step by step, field observations and data were surveyed according to the questions to solve. In order to reconstruct the facies architecture and the geometry of the basin, a number of sections have been logged in detail (see chapter 3, “The sections”). The exact biostratigraphic setting is discussed in chapter 4 (“Biostratigraphy”). Sedimentary environments range from non-marine alluvial fans and braided rivers in the basal part of the succession to deltas, storm-dominated shelf, slope and deep-marine basin. The latter comprises the largest part of the basin fill, consisting of monotonous mudstones, siltstones and proximal to distal turbidities. The only continuous carbonate unit (~30 m) locally formed at Tappenader. Other localities in which thin fossil-bearing carbonate strata occur are Torbat-e-Jam (benthic fauna) and, to a lesser extent, Ghal-e-Sangi. These rare shallow-water carbonates, which also contain corals, represent only short intervals (see chapter 5,” Facies association and sedimentary environments”). Relative changes in sea level were reconstructed on the basis of deepening- and shallowing-upward trends. Sequence boundaries and parasequences have been distinguished and analyzed in chapter 6 (“Sequence stratigraphy”). In most areas, the basin rapidly evolved from a shallow marine, transgressive succession to a deep-marine, basinal succession. The only area where shallow conditions persisted from the Late Bajocian to the Late Bathonian, and even into the Early Callovian is the Kuh-e-Radar area which corresponds to a fan-delta setting. A trace fossil analysis has been carried out to obtain additional evidence on the bathymetry of the basin (see chapter 7, “Ichnology”). Altogether 29 ichnospecies belonging to 15 ichnogenera have been identified, as well as 10 ichnogenera, which were determined only at genus level. They can be grouped in the well-known “Seilacherian ichnofacies”. Very high subsidence rates and strong lateral thickness variations suggest that the Kashafrud Formation is a rift related basin that formed as the eastern extension of the South Caspian Basin. The basin evolution is reviewed, the eastern and western continuations of the basin were checked in the field and also in the literature (see chapter 8, “Basin evolution”). In all, the present study provided new insights into the development of the Kashafrud Formation, e.g. more biostratigraphic data from the base and the top of the succession, a relatively complete picture of the trace fossil associations, a better recognition and reconstruction of the sedimentary environments in different parts of the basin. Finally this research project will be a good basis for further investigations, especially towards the west, as parts of the Kashafrud Formation are source rocks of a hydrocarbon reservoir in NE Iran.
Four sections of the Galala and Maghra El Hadida formations on the footwalls of the slopes of the northern and southern Galala plateaus in Wadi Araba (Eastern Desert) have been measured and sampled in great detail. The Galala Formation is ranging in thickness from 55 to 95 meters. It unconformably overlies the Malha Formation which forms the base of the studied sections. The upper boundary of the Galala Formation is characterized by a major unconformity which separates it from the overlying the Maghra El Hadida Formation. The Galala Formation can be subdivided into five shallowing-upward cycles, each cycle starting with deep-lagoonal, marly-silty deposits at the base and grading into highly fossiliferous shallow-lagoonal limestones at the top. Only the basal part of the Galala Formation consists of unfossiliferous, greenish sandy siltstones intercalated with thin cross-bedded, bioturbated, fine- to medium-grained sandstones. Despite the lack of biostratigraphic markers in that lower part, its age can be assigned to the late Middle Cenomanian, since the conformably overlying strata contain the ammonite Neolobites vibrayeanus (D’ORBIGNY), the index marker of the early Upper Cenomanian which extends into the top of the formation. The measured thickness of the overlying Maghra El Hadida Formation is ranging from 59 to 118 meters. This formation starts with the Ghonima Member, introduced in this work to distinguish a brown, fine- to medium-grained calcareous sandstone unit in its lower part. The Ghonima Member is erosionally incised into the Galala Formation, explaining its strong lateral variability in thickness, ranging from 3 to 21 meters. It is mostly unfossiliferous except for irregular bioturbation in its upper part. The Ghonima Member is assigned to the middle Upper Cenomanian, based on its stratigraphic position between the lower Upper Cenomanian Neolobites vibrayeanus Zone and the overlying upper Upper Cenomanian Metoicoceras geslinianum and Vascoceras cauvini zones. This means that the lower part of the Maghra El Hadida Formation, about 20 – 30 m thick, accumulated during the latest Cenomanian and that the base of the formation does not coincide with the base of the Turonian as commonly believed. The overlying succession of the Maghra El Hadida Formation is characterized by an increase of carbonate content, represented by yellow, soft marls intercalated with fine-grained wacke- to packstones containing a highly fossiliferous ammonite assemblage of the upper Upper Cenomanian and Lower Turonian (zones of Vascoceras proprium, Choffaticeras spp., and Wrightoceras munieri). The Middle Turonian part of the Maghra El Hadida Formation consists of poorly fossiliferous, thick-bedded yellowish marls with upward-increasing silt content, showing occasional intercalations of medium- to coarse-grained sandstones with hummocky cross-stratification. The topmost part of the Maghra El Hadida Formation consists of brownish, medium-grained sandstones topped by fossiliferous marly limestones yielding the Upper Turonian zonal ammonite Coilopoceras requienianum (D’ORBIGNY). Based on sequence stratigraphic analyses, four complete 3rd order depositional sequences and the lower part of a fifth one, each bounded by major unconformities, can be recognized: depositional sequence DS WA 1 (upper Middle – lower Upper Cenomanian) includes the entire Galala Formation, while the Maghra El Hadida Formation comprises all the overlying depositional sequences: DS WA 2 (upper Upper Cenomanian – Lower Turonian) reaches from the base of the Metoicoceras geslinianum Zone to the top of Wrightoceras munieri Zone, DS WA 3 and DS WA 4 comprise the Middle Turonian, while Upper Turonian sequence DS WA 5 is not complete. The stratigraphic positions of the recognized sequence 2 boundaries SB WA 1 to SB WA 5 match well with contemporaneous sequence boundaries known from Europe and elsewhere. The stacking pattern of the basic cycles and bundles of the Galala Formation (5:1) and the Maghra El Hadida Formation (4:1) strongly suggest an orbital forcing by MILANKOVITCH periodicities. The Galala Formation is composed of five 5th-order bundles which equal to ~500 kyr, each bundle equals to ~100 kyr (short eccentricity). Every bundle has five basic (6th-order) cycles, each one representing ~20 kyr (precession). Based on this precession-short eccentricity syndrome, the accumulation rate of the Galala Formation therefore accounts for about 19 cm/kyr. The rate of sea-level fall at sequence boundary SB WA 2 (equivalent to the quasi-global mid-Late Cenomanian SB Ce V) estimated is with 35 cm/kyr which can be explained only by glacio-eustasy. The Upper Cenomanian and Lower Turonian part of the Maghra El Hadida Formation is considered to equal to ~1200 kyr, based on the existence of three 4th-order bundles with an inferred duration of ~400 kyr for each bundle (long eccentricity of the MILANKOVITCH Band). Every bundle consists of four basic cycles with a duration of ~100 kyr. This means that the upper Cenomanian part of the Maghra El Hadida Formation is equivalent to ~400 kyr, while the Lower Turonian (consisting of the two upper bundles) lasted 800 kyr. This matches well with the recently proposed 785 kyr duration of the Early Turonian (SAGEMAN et al., 2006; VOIGT et al., 2008) and contradicts the 1300 kyr according to the standard time scale of GRADSTEIN et al. (2004). According to this temporal constrains, the accumulation rate of the Maghra El Hadida Formation is about 4.25 cm/kyr. In addition, based on the cyclostratigraphic analysis, the range of the Early Turonian genus Choffaticeras (HYATT) is equivalent to ~325 kyr and morphological changes within its lineage can be quantified. The macrobenthos (bivalves, gastropods, echinoids) and cephalopods of the Galala and Maghra El Hadida formations were identified and illustrated in 24 figures. The ammonite taxonomy and palaeobiogeographic distribution is discussed in detail. Four genera and eight ammonite species are recorded from Egypt for the first time. The microfloral and -faunal assemblage identified in thin sections revealed two species of dasycladalean algae, two species of udoteacean algae, five species of benthic foraminifera, and two species of crustacean microcoprolites. The six facies types of the upper Middle – Upper Cenomanian Galala Formation document largely open-lagoonal, warm water conditions, while the depositional environment of the Upper Cenomanian – Turonian Maghra El Hadida Formation (16 facies types) is suggested to range from a deep-subtidal to intertidal.
The study investigates the water resources and aquifer dynamics of the igneous fractured aquifer-system of the Troodos Mountains in Cyprus, using a coupled, finite differences water balance and groundwater modelling approach. The numerical water balance modelling forms the quantitative framework by assessing groundwater recharge and evapotranspiration, which form input parameters for the groundwater flow models. High recharge areas are identified within the heavily fractured Gabbro and Sheeted Dyke formations in the upper Troodos Mountains, while the impervious Pillow Lava promontories - with low precipitation and high evapotranspiration - show unfavourable recharge conditions. Within the water balance studies, evapotranspiration is split into actual evapotranspiration and the so called secondary evapotranspiration, representing the water demand for open waters, moist and irrigated areas. By separating the evapotranspiration of open waters and moist areas from the one of irrigated areas, groundwater abstraction needs are quantified, allowing the simulation of single well abstraction rates in the groundwater flow models. Two sets of balanced groundwater models simulate the aquifer dynamics in the presented study: First, the basic groundwater percolation system is investigated using two-dimensional vertical flow models along geological cross-sections, depicting the entire Troodos Mountains up to a depth of several thousands of metres. The deeply percolating groundwater system starts in the high recharge areas of the upper Troodos, shows quasi stratiform flow in the Gabbro and Sheeted Dyke formations, and rises to the surface in the vicinity of the impervious Pillow Lava promontories. The residence times mostly yield less than 25 years, the ones of the deepest fluxes several hundreds of years. Moreover, inter basin flow and indirect recharge of the Circum Troodos Sedimentary Succession are identified. In a second step, the upper and most productive part of the fractured igneous aquifer-system is investigated in a regional, horizontal groundwater model, including management scenarios and inter catchment flow studies. In a natural scenario without groundwater abstractions, the recovery potential of the aquifer is tested. Predicted future water demand is simulated in an increased abstraction scenario. The results show a high sensitivity to well abstraction rate changes in the Pillow Lava and Basal Group promontories. The changes in groundwater heads range from a few tens of metres up to more than one hundred metres. The sensitivity in the more productive parts of the aquifer-system is lower. Inter-catchment flow studies indicate that - besides the dominant effluent conditions in the Troodos Mountains - single reaches show influent conditions and are sub-flown by groundwater. These fluxes influence the local water balance and generate inter catchment flow. The balanced groundwater models form thus a comprehensive modelling system, supplying future detail models with information concerning boundary conditions and inter-catchment flow, and allowing the simulation of impacts of landuse or climate change scenarios on the dynamics and water resources of the Troodos aquifer-system.