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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.
Current changes of biodiversity result almost exclusively from human activities. This anthropogenic conversion of natural ecosystems during the last decades has led to the so-called ‘biodiversity crisis’, which comprises the loss of species as well as changes in the global distribution patterns of organisms. Species richness is unevenly distributed worldwide. Altogether, 17 so-called ‘megadiverse’ nations cover less than 10% of the earth’s land surface but support nearly 70% of global species richness. Mexico, the study area of this thesis, is one of those countries. However, due to Mexico’s large extent and geographical complexity, it is impossible to conduct reliable and spatially explicit assessments of species distribution ranges based on these collection data and field work alone. In the last two decades, Species distribution models (SDMs) have been established as important tools for extrapolating such in situ observations. SDMs analyze empirical correlations between geo-referenced species occurrence data and environmental variables to obtain spatially explicit surfaces indicating the probability of species occurrence. Remote sensing can provide such variables which describe biophysical land surface characteristics with high effective spatial resolutions. Especially during the last three to five years, the number of studies making use of remote sensing data for modeling species distributions has therefore multiplied. Due to the novelty of this field of research, the published literature consists mostly of selective case studies. A systematic framework for modeling species distributions by means of remote sensing is still missing. This research gap was taken up by this thesis and specific studies were designed which addressed the combination of climate and remote sensing data in SDMs, the suitability of continuous remote sensing variables in comparison with categorical land cover classification data, the criteria for selecting appropriate remote sensing data depending on species characteristics, and the effects of inter-annual variability in remotely sensed time series on the performance of species distribution models. The corresponding novel analyses were conducted with the Maximum Entropy algorithm developed by Phillips et al. (2004). In this thesis, a more comprehensive set of remote sensing predictors than in the existing literature was utilized for species distribution modeling. The products were selected based on their ecological relevance for characterizing species distributions. Two 1 km Terra-MODIS Land 16-day composite standard products including the Enhanced Vegetation Index (EVI), Reflectance Data, and Land Surface Temperature (LST) were assembled into enhanced time series for the time period of 2001 to 2009. These high-dimensional time series data were then transformed into 18 phenological and 35 statistical metrics that were selected based on an extensive literature review. Spatial distributions of twelve tree species were modeled in a hierarchical framework which integrated climate (WorldClim) and MODIS remote sensing data. The species are representative of the major Mexican forest types and cover a variety of ecological traits, such as range size and biotope specificity. Trees were selected because they have a high probability of detection in the field and since mapping vegetation has a long tradition in remote sensing. The result of this thesis showed that the integration of remote sensing data into species distribution models has a significant potential for improving and both spatial detail and accuracy of the model predictions.
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.
Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.
The Ringgold Knoll pegmatite, a late-stage member of the Granite Harbour Intrusives, crosscuts high-grade Wilson gneisses of the Oates Coast, which forms the westernmost part of the Wilson Terrane at the Pacific end of the Cambro-Ordovician Ross orogenic belt in West Antarctica. The pegmatite mineral assemblage consists of K-feldspar, plagioclase, quartz, garnet (almandinespessartine-pyrope), dark tourmaline (schorl-dravite), muscovite, apatite, monazite, zircon, blue AI-rich tourmaline and dumortierite in order of decreasing abundances. Major, minor and rare earth elements are reported for the greater part of the mineral assemblage. The time of pegmatite emplacement is constrained by Rb-Sr and Sm-Nd isochron ages of 492 ± 8 (2a) Ma and 500 ± 40 (2a) Ma, respectively. High initial 87Sr/86Sr of 0.7315 ± 0.0003 and low E Nd,t of -8.7 ± 1.2 strongly support an origin of the magma from highly evolved crustal source rocks. K-Ar and Ar-Ar model ages of about 470 to 475 Ma for igneous muscovite indicate that the pegmatite together with its wall rocks spent a prolonged period at elevated temperatures before final cooling below about 350 °C. The muscovite dates may give an estimate for the time of exhumation of the Oates Coast crystalline basement along two major late Ross orogenic detachment zones within the Wilson Terrane i.e. the Wilson and the Exiles thrusts (c.f. FLÖTTMANN and KLEINSCHMIDT, 1991).
During the Mesoproterozoic large volumes of magma were repeatedly emplaced within the basement of NW Namibia. Magmatic activity started with the intrusion of the anorthositic rocks of the Kunene Intrusive Complex (KIC) at 1,385-1,347 Ma. At its south-eastern margin the KIC was invaded by syenite dykes (1,380-1,340 Ma) and younger carbonatites (1,140-1,120 Ma) along ENE and SE trending faults. Older ferrocarbonatite intrusions, the ‘carbonatitic breccia’, frequently contain wallrock fragments, whereas subordinate ferrocarbonatite veins are almost xenolith-free. Metasomatic interaction between carbonatite-derived fluids and the neighbouring and incorporated anorthosites led to the formation of economically important sodalite deposits. Investigated anorthosite samples display the magmatic mineral assemblage of Pl (An37-75) ± Ol ± Opx ± Cpx + Ilm + Mag + Ap ± Zrn. Ilmenite and pyroxene are surrounded by narrow reaction rims of biotite and pargasite. During the subsolidus stage sporadic coronitic garnet-orthopyroxene-quartz assemblages were produced. Thermobarometry studies on amphiboles yield temperatures of 985-950°C whereas the chemical composition of coronitic garnet and orthopyroxene indicate a subsolidus re-equilibration of the KIC at conditions of 760 ± 100°C and 7.3 ± 1 kbar. In the syenites Kfs, Pl, Hbl and/or Cpx crystallized first, followed by a second generation of Kfs, Hbl, Fe-Ti oxides and Ttn. Crystallization of potassium feldspar occurred under temperatures of 890-790°C. For the crystallization of hastingsite pressures of 6.5 ± 0.6 kbar are obtained. In order to constrain the source rocks of the two suites, oxygen isotope analyses of feldspar as well as geochemical bulk rock analyses were carried out. In case of the anorthosites, the general geochemical characteristics are in excellent agreement with their derivation from fractionated basaltic liquids, with the d18O values (5.88 ± 0.19 ‰) proving their derivation from mantle-derived magmas. The results obtained for the felsic suite, provide evidence against consanguinity of the anorthosites and the syenites, i.e. (1) compositional gaps between the geochemical data of the two suites, (2) trace element data of the felsic suite points to a mixed crustal-mantle source, (3) syenites do not exhibit ubiquitous negative Eu-anomalies in their REE patterns, which would be expected from fractionation products of melts that previously formed plagioclase cumulates and (4) feldspar d18O values from the syenites fall in a range of 7.20-7.92 ‰, which, however, is about 1.6 ‰ higher than the average d18O of the anorthosites. Conformably, the crustal-derived felsic and the mantle-derived anorthositic suite are suggested to be coeval but not consanguineous. Their spatial and temporal association can be accounted for, if the heat necessary for crustal melting is provided by the upwelling and emplacement of mantle-derived melts, parental to the anorthosites. In order to constrain the source of the 1,140-1,120 Ma carbonatites and to elucidate the fenitizing processes, which led to the formation of the sodalite, detailed mineralogical and geochemical investigations, stable isotope (C,O,S) analyses and fluid inclusion measurements (microthermometrical studies and synchrotron-micro-XRF analyses) have been combined. There is striking evidence that carbonatites of both generations are magmatic in origin. They occur as dykes with cross-cutting relationships and margins disturbed by fenitic aureoles, and contain abundant flow-oriented xenoliths. The mineral assemblage of both carbonatite generations of Ank + Cal + Ilm + Mag + Bt ± Ap ± pyrochlore ± sulphides in the main carbonatite body and Ank + Cal + Mag ± pyrochlore ± rutile in the ferrocarbonatite veins, their geochemical characteristics and the O and C isotope values of ankerite (8.91 to 9.73 and –6.73 to –6.98, respectively) again indicate igneous derivation, with the 18O values suggesting minor subsolidus alteration. NaCl-rich fluids, released from the carbonatite melt mainly caused the fenitization of both, the incorporated and the bordering anorthosite. This process is characterized by the progressive transformation of Ca-rich plagioclase into albite and sodalite. Applying conventional geothermobarometry combined with fluid-inclusion isochore data, it was possible to reconstruct the P-T conditions for the carbonatite emplacement and crystallization (1200-630°C, 4-5 kbar) and for several mineral-forming processes during metasomatism (e.g. formation of sodalite: 800-530°C). The composition and evolutionary trends of the fenitizing solution were estimated from both the sequence of metasomatic reactions within wallrock xenoliths in the carbonatitic breccia and fluid inclusion data. The fenitizing solutions responsible for the transformation of albite into sodalite can be characterised as of NaCl-rich aqueous brines (19-30 wt.% NaCl eq.), that contained only minor amounts of Sr, Ba, Fe, Nb, and LREE.
In the central Alps permafrost can be expected above 2300 m a.s.l., at altitudes where mean annual air temperatures are below -1 °C. Isolated permafrost occurrences can be detected in north-exposed talus slopes, far below the timberline, where mean annual air temperatures are positive. Driving factors are assumed to be a low income of solar radiation, a thick organic layer with high insulation capacities as well as the thermally induced chimney effect.
Aim of this study is to achieve a deeper understanding of the factors determining the site-specific thermal regime, as well as the spatially limited and temporally highly variable permafrost occurrences in vegetated talus slopes.
Three supercooled talus slopes in the Swiss Alps were chosen for investigation. Substantially different characteristics were a central criterion in the selection of study sites. Located in the Upper Engadin, climatic conditions, altitude as well as dimensions of the talus slopes are comparable for the study sites Val Bever and Val Susauna; major differences are rooted in the nature of talus substrate and in humus- and vegetation distribution. Characteristics of the Brüeltobel site, located in the Appenzeller Alps, diverge with regard to climatic conditions, altitude and dimensions of the talus slope; humus- and vegetation compositions are comparable to the Val Susauna site.
Confirmation and characterisation of ground ice is accomplished by the application of electrical resistivity and seismic refraction tomography. The estimation of the spatial permafrost distribution is based on quasi-3D resistivity imaging. For the confirmation of permafrost and the analysis of its temporal variability electrical resistivity monitoring arrays were constructed and installed at all study sites, to allow year-round measurements. In addition to resistivity monitoring, the – up to now – first seismic refraction tomography winter monitoring was conducted at the Val Susauna to analyse the permafrost evolution during the winter half-year. Investigations of the ground thermal regime were based on the analysis of temperature logger data. Besides recording air- and ground surface temperatures, focus was set on the temperature evolution in vents and in the organic layer. To analyse the relationship between permafrost distribution on the one hand and humus- and vegetation distribution on the other hand, an extensive mapping of humus characteristics and vegetation composition was conducted at Val Susauna.
The existence of permafrost could be proven at all study sites. Spatially, permafrost bodies show a narrow transition to neighbouring, unfrozen areas. As observed at Val Susauna, the permafrost distribution strongly correlates with areas with exceptionally thick organic layer, high percentages of mosses and lichens in the undergrowth and dwarf grown trees. The temporal variability of permafrost has proven to be exceptionally high, with the magnitude of seasonal variations distinctly exceeding intra-annual changes. Thereby, the winter season is characterised by a significant supercooling. During snowmelt a growth in volumetric ice content is induced by refreezing of percolating meltwater on the supercooled talus.
The results confirmed the fundamental influence of the chimney effect on the existence and temporal variability of permafrost in talus slopes. Divergences in the effectiveness of the thermal regime were detected between the study sites. These are based on differences in the nature of talus material, humus characteristics and vegetation composition.
During summer, the organic material is usually dry at the daytime, inducing a high insulation capability and a protection of the subsurface against high atmospheric temperatures. Bouldery talus slopes typically show an organic layer that is fragmented by large boulders, which induces a strongly reduced insulation capability and allows an efficient heat exchange by convective airflow and percolating precipitation water. In the winter half-year, the thermal conductivity of the organic layer increases massively under moist or frozen conditions, allowing an efficient, conductive cooling of the talus material. The convective cooling in bouldery talus slopes affects an earlier onset and a higher magnitude of supercooling than under consistent humus conditions. Here, conductive heat flow is dominant and the cooling in autumn is buffered by a prolonged zero curtain. The snow cover has proven to be incapable of prohibiting an efficient supercooling of the talus slope in winter, almost independent from thickness.
The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5–5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15–30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.
The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.
In 2001 the 433 m deep Messel 2001 borehole was drilled in the centre of the Messel Pit, 25 km south of Frankfurt (Germany). Geoscientific results from this drilling clarified the origin of the circular-shaped basin as a maar-diatreme-structure. Recovered deposits consist of lacustrine sediments (0-240 m) and volcaniclastic rocks such as lapilli tuffs (240-373 m) as well as rocks of the underlying diatreme breccia (373 433 m). The lapilli tuffs, as main interest here, show little differentiation on a macro- and microscopic scale and appear as a massive and unsorted volcaniclastic body with dominating juvenile lapilli and accidental clasts mostly in the range of (sub)millimetres to centimetres in diameter. This study presents rock magnetic properties measured on core samples of the volcaniclastic units and explains the origin of downhole magnetic anomalies detected during the drilling project in 2001. Magnetic behaviour of the erupted material is related to fine-grained, Fe-rich (titano)-magnetites, which are dispersed within the juvenile lapilli. Temperature-dependent susceptibility experiments, isothermal remanent magnetisation and hysteresis investigations demonstrate similar ferrimagnetic properties throughout the volcaniclastic material, in terms of composition, coercivity and grain size (pseudo-single-domain particles) of the ferrimagnetic minerals. Thus, during emplacement of the erupted material, the ferrimagnetic minerals had the same remanence acquisition potential. However, demagnetisation experiments show different magnetic stability behaviour of the acquired natural remanent magnetisation (NRM). Heating experiments prove the acquisition of thermal remanent magnetisation (TRM) dominated by temperature effects which could have been occurred during eruption and deposition of volcanic material, forming the Messel maar-diatreme. It is assumed that the upper half of the lapilli tuffs was deposited at relatively low depositional temperatures (<300 °C), whereas the material of the lower half took advantage of higher temperatures (>>300 °C). To understand the rock magnetic character within the Messel maar-diatreme-facies, particle grain sizes, the degree of the relative fraction dominance and the shape of the juvenile fragments have been studied in more detail. Image analytical methods as well as major and trace element analyses on the juvenile fraction support the clear subdivision of the lapilli tuffs. These findings in combination with rockmagnetic data indicate a separation into a relatively hot, geochemically undifferentiated eruption phase and a colder, differentiated phase. A two-condition eruption stage at the end of the Messel volcanic activity is suggested. The juvenile particles account for the temperature evolution and heat conditions during deposition of the Messel tuffs and contribute to the origin of magnetic field anomalies. Based on gravity parameters and the results of magnetisation properties, the potential field 3D-model of the Messel subsurface explains the negative ground anomalies, calculates the mass and volume parameters of the drilled lithozones and shows the asymmetric appearance of the diatreme-structure.
Atmospheric circulation is a key driver of climate variability, and the representation of atmospheric circulation modes in regional climate models (RCMs) can enhance the credibility of regional climate projections. This study examines the representation of large‐scale atmospheric circulation modes in Coupled Model Inter‐comparison Project phase 5 RCMs once driven by ERA‐Interim, and by two general circulation models (GCMs). The study region is Western Europe and the circulation modes are classified using the Promax rotated T‐mode principal component analysis. The results indicate that the RCMs can replicate the classified atmospheric modes as obtained from ERA5 reanalysis, though with biases dependent on the data providing the lateral boundary condition and the choice of RCM. When the boundary condition is provided by ERA‐Interim that is more consistent with observations, the simulated map types and the associating time series match well with their counterparts from ERA5. Further, on average, the multi‐model ensemble mean of the analysed RCMs, driven by ERA‐Interim, indicated a slight improvement in the representation of the modes obtained from ERA5. Conversely, when the RCMs are driven by the GCMs that are models without assimilation of observational data, the representation of the atmospheric modes, as obtained from ERA5, is relatively less accurate compared to when the RCMs are driven by ERA‐Interim. This suggests that the biases stem from the GCMs. On average, the representation of the modes was not improved in the multi‐model ensemble mean of the five analysed RCMs driven by either of the GCMs. However, when the best‐performed RCMs were selected on average the ensemble mean indicated a slight improvement. Moreover, the presence of the North Atlantic Oscillation (NAO) in the simulated modes depends also on the lateral boundary conditions. The relationship between the modes and the NAO was replicated only when the RCMs were driven by reanalysis. The results indicate that the forcing model is the main factor in reproducing the atmospheric circulation.
The main purpose of volcano-seismology concerns the qualitative and quantitative description of one or more unknown seismic source(s) located at some unknown depth beneath a volcano. Even if many different volcanoes show similar seismic signal characteristics, up to now it was not possible to find a standard seismic source model for volcanoes, as the double-couple in earthquake seismology. Volcanoes with a continuous activity, like Stromboli (Italy), represent for the volcano seismologist a perfect natural laboratory to address this question. This thesis treats the study of explosion-quakes and volcanic tremor recorded on Stromboli in a broadband frequency range, and discusses the location and the possible mechanisms of the seismic source(s). Seismic and infrasonic recordings of explosion-quake from Stromboli showed that the high-frequency phase propagates with a velocity of approximately 330 m/s. The seismic source can be explained as an explosion at the top of the magma column generated by rising gas bubbles. The seismic P-wave and the air-wave are both generated in the same point at the same time. The different path lengths and velocities for the seismic wave and the air-wave result in a difference in arrival times dt, that could be used to deduce the magma level and sound speed in the eruption column inside the conduit. Stations installed near the active crater reveal that infrasonic and seismic recordings of the short-period tremor (> 1 Hz) share the same spectral content and show similar energy fluctuations. Therefore, the short-period volcanic tremor at Stromboli originates from the continuous out-bursting of small gas bubbles in the upper part of the magmatic column. The spectrum of the long-period tremor recorded at Stromboli consists of three main peaks with periods at 4.8 s, 6 s and 10 s, and amplitudes varying with the regional meteorological situation. Hence, they are not generated by a close volcanic source but rather by ocean microseisms (OMS). The passage of a local cyclone seems to be the seismic source for spectral energy at 4.8 s and 10 s, which represent the Double Frequency and the Primary Frequency of the OMS, respectively. Concerning the 6 s peak, a cyclone near the British Isles could act as a seismic source. Seismic data from the first broadband array deployed on Stromboli showed surprisingly simple waveforms, indicating an initially contracting source mechanism. The analysis of particle motion and the application of seismic array techniques allowed the location of a seismic source in the shallow part of the volcano. Eruption parameters and seismic source characteristics of the April 5, 2003 Stromboli eruption have been estimated using different inversion approaches. The paroxysm was triggered by a shallow slow thrust-faulting dislocation event with a moment magnitude of Mw = 3.0 and possibly associated with a crack that formed previously by dike extrusion. At least one blow-out phase during the paroxysmal explosion could be identified from seismic signals with an equivalent moment magnitude of Mw = 3.7. It can be represented by a vertical linear vector dipole and two weaker horizontal linear dipoles in opposite direction, plus a vertical force. Seismic measurements performed during controlled and reproducible blow-out experiments with a gas volume entrapped in basaltic melt revealed the following: Monochromatic seismic signals suggest a blow-out in a more ductile regime, whereas broader frequency content indicates rupture in a more brittle environment. The longer the crucible, the weaker the seismic signals. An increase in pressure results in a stronger fragmentation, but not in a higher ejection velocity of the plug neither in a higher seismic amplitude. Even if the very long period observations like the tilt signal could not be simulated in the laboratory, the blow-out experiments simulate very well the short-period seismic signals recorded at Stromboli volcano.
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.
Numerous ephemeral rivers and thousands of natural pans characterize the transboundary Iishana-System of the Cuvelai Basin between Namibia and Angola. After the rainy season, surface water stored in pans is often the only affordable water source for many people in rural areas. High inter- and intra-annual rainfall variations in this semiarid environment provoke years of extreme flood events and long periods of droughts. Thus, the issue of water availability is playing an increasingly important role in one of the most densely populated and fastest growing regions in southwestern Africa. Currently, there is no transnational approach to quantifying the potential storage and supply functions of the Iishana-System. To bridge these knowledge gaps and to increase the resilience of the local people's livelihood, suitable pans for expansion as intermediate storage were identified and their metrics determined. Therefore, a modified Blue Spot Analysis was performed, based on the high-resolution TanDEM-X digital elevation model. Further, surface area–volume ratio calculations were accomplished for finding suitable augmentation sites in a first step. The potential water storage volume of more than 190,000 pans was calculated at 1.9 km\(^3\). Over 2200 pans were identified for potential expansion to facilitate increased water supply and flood protection in the future.
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.
Sufficient plant-available water is one of the most important requirements for vital, stable, and well-growing forest stands. In the face of climate change, there are various approaches to derive recommendations considering tree species selection based on plant-available water provided by measurements or simulations. Owing to the small-parcel management of Central European forests as well as small-spatial variation of soil and stand properties, in situ data collection for individual forest stands of large areas is not feasible, considering time and cost effort. This problem can be addressed using physically based modeling, aiming to numerically simulate the water balance. In this study, we parameterized, calibrated, and verified the hydrological multidimensional WaSiM-ETH model to assess the water balance at a spatial resolution of 30 m in a German forested catchment area (136.4 km2) for the period 2000–2021 using selected in situ data, remote sensing products, and total runoff. Based on the model output, drought-sensitive parameters, such as the difference between potential and effective stand transpiration (Tdiff) and the water balance, were deduced from the model, analyzed, and evaluated. Results show that the modeled evapotranspiration (ET) correlated significantly (R2 = 0.80) with the estimated ET using MODIS data (MOD16A2GFv006). Compared with observed daily, monthly, and annual runoff data, the model shows a good performance (R2: 0.70|0.77|0.73; Kling–Gupta efficiency: 0.59|0.62|0.83; volumetric efficiency: 0.52|0.60|0.83). The comparison with in situ data from a forest monitoring plot, established at the end of 2020, indicated good agreement between observed and simulated interception and soil water content. According to our results, WaSiM-ETH is a potential supplement for forest management, owing to its multidimensionality and the ability to model soil water balance for large areas at comparable high spatial resolution. The outputs offer, compared to non-distributed models (like LWF-Brook90), spatial differentiability, which is important for small-scale parceled forests, regarding stand structure and soil properties. Due to the spatial component offered, additional verification possibilities are feasible allowing a reliable and profound verification of the model and its parameterization.
Advances in remote inventory and analysis of forest resources during the last decade have reached a level to be now considered as a crucial complement, if not a surrogate, to the long-existing field-based methods. This is mostly reflected in not only the use of multiple-band new active and passive remote sensing data for forest inventory, but also in the methodic and algorithmic developments and/or adoptions that aim at maximizing the predictive or calibration performances, thereby minimizing both random and systematic errors, in particular for multi-scale spatial domains. With this in mind, this editorial note wraps up the recently-published Remote Sensing special issue “Remote Sensing-Based Forest Inventories from Landscape to Global Scale”, which hosted a set of state-of-the-art experiments on remotely sensed inventory of forest resources conducted by a number of prominent researchers worldwide.
Digital platforms, such as Amazon, represent the major beneficiaries of the Covid‐19 crisis. This study examines the role of digital platforms and their engagement in digitalisation initiatives targeting (small) brick‐and‐mortar retailers in Germany, thereby contributing to a better understanding of how digital platforms augment, substitute or reorganise physical retail spaces. This study applies a mixed‐method approach based on qualitative interviews, participant observation as well as media analysis. First, the study illustrates the controversial role of digital platforms by positioning themselves as supporting partners of the (offline) retailers, while simultaneously shifting power towards the platforms themselves. Second, digital platforms have established themselves not only as infrastructure providers but also as actors within these infrastructures, framing digital as well as physical retail spaces, inter alia due to their role as publicly legitimised retail advisers. Third, while institutions want to help retailers to survive, they simultaneously enhance retailers' dependency on digital platforms.
By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.
Increasing urbanisation is one of the biggest pressures to vegetation in the City of Cape Town. The growth of the city dramatically reduced the area under indigenous Fynbos vegetation, which remains in isolated fragments. These are subject to a number of threats including atmospheric deposition, atypical fire cycles and invasion by exotic plant and animal species. Especially the Port Jackson willow (Acacia saligna) extensively suppresses the indigenous Fynbos vegetation with its rapid growth.
The main objective of this study was to investigate indicators for a quick and early prediction of the health of the remaining Fynbos fragments in the City of Cape Town with help of remote sensing.
First, the productivity of the vegetation in response to rainfall was determined. For this purpose, the Enhanced Vegetation Index (EVI), derived from Terra MODIS data with a spatial resolution of 250m, and precipitation data of 19 rainfall stations for the period from 2000 till 2008 were used. Within the scope of a flexible regression between the EVI data and the precipitation data, different lags of the vegetation response to rainfall were analysed. Furthermore, residual trends (RESTREND) were calculated, which result from the difference between observed EVI and the one predicted by precipitation. Negative trends may suggest a degradation of the habitats. In addition, the so-called Rain-use Efficiency (RUE) was tested in this context. It is defined as the ratio between net primary production (NPP) – represented by the annual sum of EVI – and the annual rainfall sum. These indicators were analysed for their suitability to determine the health of the indigenous Fynbos vegetation.
Furthermore, the degree of dispersal of invasive species especially the Acacia saligna was investigated. With the specific characteristics of the tested indicators and the spectral signature of Acacia saligna, i.e. its unique reflectance over the course of the year, the dispersal was estimated. Since the growth of invasive species dramatically reduces the biodiversity of the fragments, their presence is an important factor for the condition of ecosystem health.
This work focused on 11 test sites with an average size of 200ha, distributed over the whole area of the City of Cape Town. Five of these fragments are under conservation and the others shall be protected in the near future, too, which makes them of special interest. In January 2010, fieldwork was undertaken in order to investigate the state and composition of the local vegetation.
The results show promising indicators for the assessment of ecosystem health. The coefficients of determination of the EVI-rainfall regression for Fynbos are minor, because the reaction of this vegetation type to rainfall is considerably lower than the one of the invasive species. Thus, a good distinction between indigenous and alien vegetation is possible on the basis of this regression. On the other hand, the RESTREND method, for which the regression forms the basis, is only of limited use, since the significance of these trends is not given for Fynbos vegetation. Furthermore, the RUE has considerable potential for the assessment of ecosystem health in the study area. The Port Jackson willow has an explicitly higher EVI than the Fynbos vegetation and thus its RUE is more efficient for a similar amount of rainfall. However, it has to be used with caution, because local and temporal variability cannot be extinguished in the study area over the rather short MODIS time series.
These results display that the interpretation of the indicators has to be conducted differently from the literature, because the element of invasive species was not considered in most of the previous papers. An increase in productivity is not necessarily equivalent with an improvement in health of the fragment, but can indicate a dispersal of Acacia saligna. This shows the general problem of the term ‘degradation’ which in most publications so far is only measured by productivity and other factors like invasive species are disregarded.
On the basis of the EVI-rainfall regression and statistical measures of the EVI, the distribution of invasive species could be delineated. Generally, a strong invasion of the Port Jackson willow was discovered on the test sites. The results display that a reasoned and sustainable management of the fragments is essential in order to prevent the suppression of the indigenous Fynbos vegetation by Acacia saligna. For this purpose, remote sensing can give an indication which areas changed so that specific field surveys can be undertaken and subsequent management measures can be determined.
Wind energy is a key option in global dialogues about climate change mitigation. Here, we combined observations from surface wind stations, reanalysis datasets, and state‐of‐the‐art regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX Africa) to study the current and future wind energy potential in Zambia. We found that winds are dominated by southeasterlies and are rarely strong with an average speed of 2.8 m·s\(^{−1}\). When we converted the observed surface wind speed to a turbine hub height of 100 m, we found a ~38% increase in mean wind speed for the period 1981–2000. Further, both simulated and observed wind speed data show statistically significant increments across much of the country. The only areas that divert from this upward trend of wind speeds are the low land terrains of the Eastern Province bordering Malawi. Examining projections of wind power density (WPD), we found that although wind speed is increasing, it is still generally too weak to support large‐scale wind power generation. We found a meagre projected annual average WPD of 46.6 W·m\(^{−2}\). The highest WPDs of ~80 W·m\(^{−2}\) are projected in the northern and central parts of the country while the lowest are to be expected along the Luangwa valley in agreement with wind speed simulations. On average, Zambia is expected to experience minor WPD increments of 0.004 W·m\(^{−2}\) per year from 2031 to 2050. We conclude that small‐scale wind turbines that accommodate cut‐in wind speeds of 3.8 m·s\(^{−1}\) are the most suitable for power generation in Zambia. Further, given the limitations of small wind turbines, they are best suited for rural and suburban areas of the country where obstructions are few, thus making them ideal for complementing the government of the Republic of Zambia's rural electrification efforts.
The production of commodities such as cocoa, rubber, oil palm and cashew, is the main driver of deforestation in West Africa (WA). The practiced production systems correspond to a land managment approach referred to as agroforestry systems (AFS), which consist of managing trees and crops on the same unit of land.Because of the ubiquity of trees, AFS reported as viable solution for climate mitigation; the carbon sequestrated by the trees could be estimated with remote sensing (RS) data and methods and reported as emission reduction efforts. However, the diversity in AFS in relation to their composition, structure and spatial distribution makes it challenging for an accurate monitoring of carbon stocks using RS. Therefore, the aim of this research is to propose a RS-based approach for the estimation of carbon sequestration in AFS across the climatic regions of WA. The main objectives were to (i) provide an accurate classification map of AFS by modelling the spatial distribution of the classification error; (ii) estimate the carbon stock of AFS in the main climatic regions of WA using RS data; (iii) evaluate the dynamic of carbon stocks within AFS across WA. Three regions of interest (ROI) were defined in Cote d'Ivoire and Burkina Faso, one in each climatic region of WA namely the Guineo-Congolian, Guinean and Sudanian, and three field campaigns were carried out for data collection. The collected data consisted of reference points for image classification, biometric tree measurements (diameter, height, species) for biomass estimation. A total of 261 samples were collected in 12 AFS across WA. For the RS data, yearly composite images from Sentinel-1 and -2 (S1 and S2), ALOS-PALSAR and GEDI data were used. A supervised classification using random forest (RF) was implemented and the classification error was assessed using the Shannon entropy generated from the class probabilities. For carbon estimation, different RS data, machine learning algorithms and carbon reference sources were compared for the prediction of the aboveground biomass in AFS. The assessment of the carbon dynamic was carried between 2017 and 2021. An average carbon map was genrated and use as reference for the comparison of annual carbon estimations, using the standard deviation as threshold. As far as the results are concerned, the classification accuracy was higher than 0.9 in all the ROIs, and AFS were mainly represented by rubber (38.9%), cocoa (36.4%), palm (10.8%) in the ROI-1, mango (15.2%) and cashew (13.4%) in ROI-2, shea tree (55.7%) and African locust bean (28.1%) in ROI-3. However, evidence of misclassification was found in cocoa, mango, and shea butter. The assessment of the classification error suggested that the error level was higher in the ROI-3 and ROI-1. The error generated from the entropy was able to reduced the level of misclassification by 63% with 11% of loss of information. Moreover, the approach was able to accuretely detect encroachement in protected areas. On carbon estimation, the highest prediction accuracy (R²>0.8) was obtained for a RF model using the combination of S1 and S2 and AGB derived from field measurements. Predictions from GEDI could only be used as reference in the ROI-1 but resulted in a prediction error was higher in cashew, mango, rubber and cocoa plantations, and the carbon stock level was higher in African locust bean (43.9 t/ha), shea butter (15 t/ha), cashew (13.8 t/ha), mango (12.8 t/ha), cocoa (7.51 t/ha) and rubber (7.33 t/ha). The analysis showed that carbon stock is determined mainly by the diameter (R²=0.45) and height (R²=0.13) of trees. It was found that crop plantations had the lowest biodiversity level, and no significant relationship was found between the considered biodiversity indices and carbon stock levels. The assessment of the spatial distribution of carbon sources and sinks showed that cashew plantations are carbon emitters due to firewood collection, while cocoa plantations showed the highest potential for carbon sequestration. The study revealed that Sentinel data could be used to support a RS-based approach for modelling carbon sequestration in AFS. Entropy could be used to map crop plantations and to monitor encroachment in protected areas. Moreover, field measurements with appropriate allometric models could ensure an accurate estimation of carbon stocks in AFS. Even though AFS in the Sudanian region had the highest carbon stocks level, there is a high potential to increase the carbon level in cocoa plantations by integrating and/or maintaining forest trees.
Previous work on Jurassic bivalves from the Iberian Range is reviewed, whereby emphasis is placed on Callovian-Kimmeridgian species. The taxonomy, distribution pattern and ecology of the bivalve fauna occurring in Middle and Upper Jurassic rocks of the Aragonian Branch of the Iberian Range have been analysed. For this purpose 14 sections and 5 additional outcrops, selected according to the abundance of bivalves, were measured in detail and sampled. The rocks studied belong to the Chelva, Yátova, Sot de Chera and Loriguilla formations of Callovian-Kimmeridgian age. The distribution of species of bivalves is given for each section. More than 3000 specimens of bivalves representing 83 species that belong to 46 genera and subgenera of the subclasses Palaeotaxodonta, Pteriomorphia, Isofilibranchia. Palaeoheterodonta, Heterodonta and Anomaldesmata have been used for the taxonomic analysis. One species is new: Plagiostoma fuersichi from the Callovian of the Chelva Fm. The autecology (trophic group and life habit) of each bivalve has been discussed. 49 samples of four sections habe been selected for a quantitative palaeoecological analysis of the bivalve fraction of the benthic fauna. Five bivalve associations and two assemblages are recognised by a Q-mode hierarchical cluster analysis (Ward method). The main environmental factors controlling bivalve associations are thought to be substrate, water energy and distribution of organic matter. The bivalves exhibit a distinct spatial and temporal distribution pattern within the Aragonian Branch. Four of the bivalve associations occur in the Upper Oxfordian (Sot de Chera Fm) and one association in the Lower Callovian (Chelva Fm). In the Sot de Chera and Loriguilla formations, the abundance of bivalves decreases from NW to SE i.e., from relatively close to the shore line towards the distal-most part of the carbonate platform. In the Chelva Fm. bivalves are abundant in the Ariño region, interpreted as a palaeogeographic high. The distribution of bivalves might have been largely controlled by the availability of nutrients.
The high-grade metamorphic Epupa Complex (EC) of north-western Namibia constitutes the south-western margin of the Archean to Proterozoic Congo Craton. The north-eastern portion of the EC has been geochemically and petrologically investigated in order to reconstruct its tectono-metamorphic evolution. Two distinct metamorphic units have been recognized, which are separated by ductile shear zones: (1) Upper amphibolite facies rocks (Orue Unit) and (2) ultrahigh-temperature (UHT) granulite facies rocks (Epembe Unit). The rocks of the EC are transsected by a large anorthosite massif, the Kunene Intrusive Complex (KIC). The Orue Unit and the Epembe Unit were affected by two distinct Mesoproterozoic metamorphic events, as is evident from differences in their metamorphic grade, in the P-T paths and in the age of peak-metamorphism: (1) The Orue Unit consists of a Palaeoproterozoic volcano-sedimentary sequence, which was intruded by large masses of I-type granitoids and by rare mafic dykes. During the Mesoproterozoic (1390-1318 Ma) the Orue Unit rocks underwent upper amphibolite facies metamorphism. The volcano-sedimentary sequence is constituted by interlayered basaltic amphibolites and rhyolitic felsic gneisses, with intercalations of migmatitic metagreywackes, migmatitic metapelites, metaarkoses and calc-silicate rocks. The Orue Unit was subdivided into three parts, which record similar heating-cooling paths but represent individual crustal levels: Heating led to the partial replacement of amphibole, biotite and muscovite through dehydration melting reactions. The peak-metamorphic P-T conditions of c. 700°C, 6.5 +/- 1.0 kbar (south-eastern part), c. 820°C, 8 +/- 0.5 kbar (south-western part) and c. 800°C, 6.0 +/- 1.0 kbar (northern part) correlate well with the mineral assemblage in the metapelites, i.e. Grt-Bt-Sil gneisses and schist in the south-eastern and south-western region and (Grt-)Crd-Bt gneisses in the northern part. Peak-metamorphism was followed by retrograde cooling to middle amphibolite facies conditions. Contact metamorphism, related with the intrusion of the anorthosites, is restricted to the direct contact to the KIC and recorded by massive metapelitic Grt-Sil-Crd felses, formed under upper amphibolite facies conditions (c. 750°C, c. 6.5 kbar). (2) The Epembe Unit consists of a Palaeoproterozoic volcano-sedimentary succession, which was intruded by small bodies of S-type granitoids and by andesitic dykes. All these rocks underwent UHT granulite facies metamorphism during the early Mesoproterozoic (1520-1447 Ma). The volcano-sedimentary succession is dominated by interlayered basaltic two-pyroxene granulites and rhyolitic felsic granulites. Migmatitic metapelites and metagreywackes are intercalated in the metavolcanites. Sapphirine-bearing MgAl-rich gneisses occur as restitic schlieren in the migmatitic metagreywackes. Reconstructed anti-clockwise P-T paths are subdivided into several distinct stages: During prograde near-isobaric heating to UHT conditions at c. 7 kbar biotite- or hornblende-bearing mineral assemblages were almost completely replaced by anhydrous mineral assemblages through various dehydration melting reactions. A subsequent pressure increase of 2-3 kbar led to the formation of the peak-metamorphic mineral assemblages Grt-Opx and (Grt-)Opx-Cpx in the orthogneisses and Grt-Opx, Grt-Sil and (Grt-)(Spr-)Opx-Sil-Qtz in the paragneisses. UHT-Metamorphism is proved by conventional geothermobarometry (970 +/- 70°C; 9.5 +/- 2.5 kbar), by the very high Al content of peak-metamorphic orthopyroxene (up to 11.9 wt.% Al2O3) in many paragneisses and by Opx-Sil-Qtz assemblages in the MgAl-rich gneisses. Post-peak decompression is recorded by several corona and symplectite textures, formed at the expense of the peak-metamorphic phases: Initial UHT decompression of about ca. 2 kbar to 940 +/- 60°C at 8 +/- 2 kbar is mainly evident from the formation of sapphirine-bearing symplectites in the Opx-Sil gneisses. Subsequent high-temperature decompression to 6 +/- 2 kbar at 800 +/- 60°C resulted in the formation of Crd-Opx-Spl, Crd-Opx and Spl-Crd symplectites. Subsequent near-isobaric cooling to upper amphibolite conditions of 660 +/- 30°C at 5 +/- 1.5 kbar led to the re-growth of biotite, hornblende, sillimanite and garnet. During continued decompression orthopyroxene and cordierite were formed at the expense of biotite in several paragneisses. In a geodynamic model UHT metamorphism of the Epembe Unit is correlated with the formation of a large magma chamber at the mantle-crust boundary, which forms the source for the anorthosites of the KIC. In contrast, amphibolite facies metamorphism of the Orue Unit is ascribed to a regional contact metamorphic event, caused by the emplacement of the anorthositic crystal mushes in the middle crust.
Burn severity was measured within the Mediterranean sclerophyll forests of south-west Western Australia (WA) using remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The region of south-west WA is considered as a high fire prone landscape and is managed by the state government’s Department of Conservation and Land Management (CALM). Prescribed fuel reduction burning is used as a management tool in this region. The measurement of burn severity with remote sensing data focused on monitoring the success and impact of prescribed burning and wildfire in this environment. The high temporal resolution of MODIS with twice daily overpasses in this area was considered highly favourable, as opportunities for prescribed burning are temporally limited by climatic conditions. The Normalised Burn Ratio (NBR) was investigated to measure burn severity in the forested area of south-west WA. This index has its heritage based on data from the Landsat TM/ETM+ sensors (Key and Benson, 1999 [1],[2]) and was transferred from Landsat to MODIS data. The measurement principally addresses the biomass consumption due to fire, whereas the change detected between the pre-fire image and the post-fire image is quantified by the ÄNBR. The NBR and the Normalised Difference Vegetation Index (NDVI) have been applied to MODIS and Landsat TM/ETM+ data. The spectral properties and the index values of the remote sensing data have been analysed within different burnt areas. The influence of atmospheric and BRDF effects on MODIS data has been investigated by comparing uncorrected top of atmosphere reflectance and atmospheric and BRDF corrected reflectance. The definition of burn severity classes has been established in a field trip to the study area. However, heterogeneous fire behaviour and patchy distribution of different vegetation structure made field classification difficult. Ground truth data has been collected in two different types of vegetation structure present in the burnt area. The burn severity measurement of high resolution Landsat data was assessed based on ground truth data. However, field data was not sufficient for rigorous validation of remote sensing data. The NBR index images of both sensors have been calibrated based on training areas in the high resolution Landsat image. The burn severity classifications of both sensors are comparable, which demonstrates the feasibility of a burn severity measurement using moderate spatial resolution 250m MODIS data. The normalisation through index calculation reduced atmospheric and BRDF effects, and thus MODIS top of at-mosphere data has been considered suitable for the burn severity measurement. The NBR could not be uniformly applied, as different structures of vegetation influenced the range of index values. Furthermore, the index was sensitive to variability in moisture content. However, the study concluded that the NBR on MODIS data is a useful measure of burn severity in the forested area of south-west WA.
The 2010 eruption of Eyjafjallajokull volcano was characterized by pulsating activity. Discrete ash bursts merged at higher altitude and formed a sustained quasi-continuous eruption column. High-resolution near-field videos were recorded on 8-10 May, during the second explosive phase of the eruption, and supplemented by contemporary aerial observations. In the observed period, pulses occurred at intervals of 0.8 to 23.4 s (average, 4.2 s). On the basis of video analysis, the pulse volume and the velocity of the reversely buoyant jets that initiated each pulse were determined. The expansion history of jets was tracked until the pulses reached the height of transition from a negatively buoyant jet to a convective buoyant plume about 100 m above the vent. Based on the assumption that the density of the gas-solid mixture making up the pulse approximates that of the surrounding air at the level of transition from the jet to the plume, a mass flux ranging between 2.2 and 3.5 . 10\(^4\) kg/s was calculated. This mass eruption rate is in good agreement with results obtained with simple models relating plume height with mass discharge at the vent. Our findings indicate that near-field measurements of eruption source parameters in a pulsating eruption may prove to be an effective monitoring tool. A comparison of the observed pulses with those generated in calibrated large-scale experiments reveals very similar characteristics and suggests that the analysis of near-field sensors could in the future help to constrain the triggering mechanism of explosive eruptions.
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.
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.
Mapping aquaculture ponds for the coastal zone of Asia with Sentinel-1 and Sentinel-2 time series
(2021)
Asia dominates the world's aquaculture sector, generating almost 90 percent of its total annual global production. Fish, shrimp, and mollusks are mainly farmed in land-based pond aquaculture systems and serve as a primary protein source for millions of people. The total production and area occupied for pond aquaculture has expanded rapidly in coastal regions in Asia since the early 1990s. The growth of aquaculture was mainly boosted by an increasing demand for fish and seafood from a growing world population. The aquaculture sector generates income and employment, contributes to food security, and has become a billion-dollar industry with high socio-economic value, but has also led to severe environmental degradation. In this regard, geospatial information on aquaculture can support the management of this growing food sector for the sustainable development of coastal ecosystems, resources, and human health. With free and open access to the rapidly growing volume of data from the Copernicus Sentinel missions as well as machine learning algorithms and cloud computing services, we extracted coastal aquaculture at a continental scale. We present a multi-sensor approach that utilizes Earth observation time series data for the mapping of pond aquaculture within the entire Asian coastal zone, defined as the onshore area up to 200 km from the coastline. In this research, we developed an object-based framework to detect and extract aquaculture at a single-pond level based on temporal features derived from high-spatial-resolution SAR and optical satellite data acquired from the Sentinel-1 and Sentinel-2 satellites. In a second step, we performed spatial and statistical data analyses of the Earth-observation-derived aquaculture dataset to investigate spatial distribution and identify production hotspots at various administrative units at regional, national, and sub-national scale.
Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016–2016/2017, 2016/2017–2017/2018 and 2017/2018–2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions.
Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.
Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species-environment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra- and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.
Sand ramps have been (and still are) neglected in geomorphological research. Only recently any awareness of their potential of being a major source of palaeoenvironmental information, thanks to their multi-process character, has been developed. In Namibia, sand ramps were terra incognita. This study defines, classifies and systematizes sand ramps, investigates the formative processes and examines their palaeoenvironmental significance. The study region is located between the coastal Namib desert and the Great Escarpment, between the Tiras Mountains to the north and the Aus area to the south. Two lines of work were followed: geomorphological and sedimentological investigations in the field, assisted by interpretation of satellite images, aerial photographs and topographic maps, and palaeopedological and sedimentological analytical work in the laboratory. Two generations of sand ramps could be identified. The older generation, represented by a single sand ramp within the study region, is characterized by the presence of old basal sediments. The bulk of the sand ramps is assigned to the young generation, which is divided into three morpho-types: in windward positions voluminous ramps are found, in leeward positions low-volume ramps exist, either of very high or very low slope angle. The most distinct characteristic of sand ramp sediments is their formation by interacting aeolian deposition and fluvial slope wash. The last period of deposition, which shaped all the entire young sand ramps, but also the upper part of the old ramp, is suggested to have occurred after c. 40 ka BP, implying a highly dynamic climatic system during that time, with seasonal aridity and low-frequency, but high-intensity rainfall. A phase of environmental stability followed, most likely around 25 ka BP, supporting growth of vegetation, stabilization and consolidation of the sediments as well as soil formation. Subsequently, the profile was truncated and a desert pavement formed, under climatic conditions comparable to those of the present semi-desert. The ramps were then largely cut off from the bedrock slopes, implying a change towards higher ecosystem variability. As the final major process, recent and modern aeolian sands accumulated on the upper ramp slopes. A luminescence date for the recent sand places their deposition at about 16 ka BP, close to the Last Glacial Maximum. Regarding the source of the sands, a local origin is proposed. For the sand ramp of the old generation the "basic cycle" of initial deposition, stabilization and denudation occurred twelve times, including a phase of calcrete and/or root-cast formation in each of them, adding up to around 60 changes in morphodynamics altogether. At least nine of these cycles took place between 105 ka BP and the LGM, indicating that the general cooling trend during the Late Pleistocene was subject to a high number of oscillations of the environmental conditions not identified before for southern Namibia. Due to the high resolution obtained by the study of sand ramp sediments, but also due to the very special situation of the study area in a desert margin, 100 km from the South Atlantic and in the transition zone between summer and winter rainfall, correlation with stratigraphies (of mostly lower resolution) established for different regions in southern Africa did not appear promising. In conclusion, sand ramps generally serve as a valuable tool for detailed deciphering of past morphodynamics and thereby palaeoenvironmental conditions. For south-west Namibia, sand ramps shed some more light on the Late Quaternary landscape evolution.
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.
Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data.
In north-western Namibia the fills of the Karoo-Etendeka depositories can be subdivided into (1) a Carboniferous-Permian, (2) a Triassic-Jurassic and (3) a Cretaceous megasequence, each recording extensional periods related to successive rifting phases in the evolving South Atlantic. The tectonic environment of the depositories in north-western Namibia changes successively from the coast towards the continental interior, which is reflected by the facies distribution and the position of time-stratigraphic gaps. Close to the present-day coastline synsedimentary listric faults, trending parallel to the South Atlantic rift (N-S), caused the formation of wedge shaped sediment bodies. Here, the Karoo Supergroup is only represented by the Permian succession in the Huab area. A hiatus within the Permian can be recognised by the correlation with the main Karoo Basin in South Africa and the Brazilian Paraná Basin. This stratal gap correlates with a pre-Beaufort Group unconformity in the main Karoo Basin that might be related to an orogenic pulse in the Cape Fold Belt. The Permian succession itself is unconformably overlain by the Lower Cretaceous Etendeka Group. This hiatus extending from the Upper Permian to the Lower Cretaceous has probably been induced by a combination of rift shoulder uplift and additional crustal doming associated with Etendeka flood volcanism. The enhanced tectonism during the Early Cretaceous controlled accommodation space for the alluvial-fluvial and aeolian deposits of the lower Etendeka Group. Disconformities within those deposits and the overlying lava succession attribute to distinct phases of tectonic and volcanic activity heralding the South Atlantic breakup. Towards the south-east, the Karoo succession becomes successively more complete. In the vicinity of Mt. Brandberg Early Triassic strata (Middle Omingonde Formation) follow disconformably above the Upper Permian/Lowermost Triassic Doros Formation. The sedimentation there was essentially controlled by the SW-NE trending Damaraland Uplift. South of the Damaraland Uplift the SW-NE trending Waterberg-Omaruru Fault zone is interpreted as a sinistral oblique-slip fault that compartmentalised the South Atlantic rift. This fault controlled accommodation space of the entire Triassic Omingonde Formation and the Early Jurassic Etjo Formation in its associated pull-apart and transtension structures. A locally well developed angular unconformity defines a hiatus between the two formations. Correlation with the main Karoo Basin in South Africa confirms that this gap is of a regional extent and not only a local, fault induced feature. Furthermore, it might also correlate with an orogenic pulse of the Cape Fold Belt. In general, the Mesozoic megasequences record the long-lived history of the southern Atlantic rift evolution. Rifting has been controlled by orogenic pulses derived from the Samfrau active margin throughout the Mesozoic. The associated intracratonic E-W extension caused the formation of grabens and conjugated oblique-slip zones. The generation of voluminous flood basalts marks the climax of intracratonic extension that was accompanied by enhanced uplift of the rift shoulders.
K-Ar dating on hornblendes and micas from the TepläDomazlice zone revealed a pattern of dates which significantly deviates from the mid-Carboniferous to early Permian one that is found in the adjacent low-pressure metamorphic Moldanubian and Saxothuringian. Especially for the Mariänske Läzne metabasic complex, confirming early Czech determinations, the dates resemble the early Devonian pattern determined for the Münchberg Gneiss Massif and the Erbendorf-Vohenstrauß zone of northeastern Bavaria. This supports the idea that all three units are remnants of a huge complex which suffered a metamorphic overprint under medium-pressure conditions, probably in the early Devonian. Streng rejuvenation is found in the southern part of the Teplä-Domailice zone by which micas and even two hornblendes were reset to mid-Carboniferous ages. According to the geological setting, part of the apparently preDevonian dates may be explained by inherited argon from earlier metamorphic and magmatic events, e.g. the high-pressure metamorphism documented in eclogitic relics. However, excess argon, caused by the mid-Carboniferous overprint cannot be excluded.
K-Ar dating on hornblendes and micas from the Tepla Domazlice zone revealed a pattern of dates which significantly deviates from the mid-Carboniferous to early Permian one that is found in the adjacent low-pressure metamorphic Moldanubian and Saxothuringian. Especially for the Marianske Lazne metabasic complex, confirming early Czech determinations, the dates resemble the early Devonian pattern determined for the Munchberg Gneiss Massif and the Erbendorf-Vohenstrau zone of northeastern Bavaria. This supports the idea that all three units are remnants of a huge' complex which suffered a metamorphic overprint under medium-pressure conditions, probably in the early Devonian. Strong rejuvenation is found in the southern part of the Tepla-Domazlice zone by which micas and even two hornblendes were reset to mid-Carboniferous ages. According to the geological setting, part of the apparently preDevonian dates may be explained by inherited argon from earlier metamorphic and magmatic events, e.g. the high-pressure metamorphism documented in eciogitic relics. However, excess argon, caused by the mid-Carboniferous overprint cannot be excluded.
The use of inverse methods allow efficient model calibration. This study employs PEST to calibrate a large catchment scale transient flow model. Results are demonstrated by comparing manually calibrated approaches with the automated approach. An advanced Tikhonov regularization algorithm was employed for carrying out the automated pilot point (PP) method. The results indicate that automated PP is more flexible and robust as compared to other approaches. Different statistical indicators show that this method yields reliable calibration as values of coefficient of determination (R-2) range from 0.98 to 0.99, Nash Sutcliffe efficiency (ME) range from 0.964 to 0.976, and root mean square errors (RMSE) range from 1.68 m to 1.23 m, for manual and automated approaches, respectively. Validation results of automated PP show ME as 0.969 and RMSE as 1.31 m. The results of output sensitivity suggest that hydraulic conductivity is a more influential parameter. Considering the limitations of the current study, it is recommended to perform global sensitivity and linear uncertainty analysis for the better estimation of the modelling results.
This work presents the analysis, 3D modeling and interpretation of gravity and aeromagnetic data of Jordan and Middle East. The potential field data delineate the location of the major faults, basins, swells, anticlines, synclines and domes in Jordan. The surface geology of Jordan and the immediate area east of the Rift is dominated by two large basins, the Al-Jafr basin in the south and the Al-Azraq-Wadi as Sirhan basin to the northeast. These two basins strike southeast-northwest and are separated by an anticlinal axis, the Kilwah-Bayir swell. The Karak Wadi El Fayha fault system occurs along the western flank of the swell. The Swaqa fault occurs on the southwest hinge of Al-Azraq basin and the Fuluq fault occurs on its northeast hinge. In the south west of Jordan, Wadi Utm-Quwaira and Disi-Mudawara fault zones are shown clearly in the aeromagnetic and gravity maps. The previous major faults are well correlated with the structural map of Jordan published by Bender (1968). 3D modeling of gravity data in the Dead Sea basin (DSB) was used together with existing geological and geophysical information to give a complete structural picture of the basin. The 3D models of the DSB show that the internal structure of the Dead Sea basin (DSB) is controlled by longitudinal faults and the basin is developed as a full graben bounded by sub-vertical faults along its long sides. In the northern planes of the 3D model, the accumulation of Quaternary (salt and marl) and Mesozoic (pre-rift) sediments are thinner than in the central and southern planes of the model. In the northern planes, the thickness of the Quaternary sediments is about 4 km, 5 km in the southern planes and it exceeds 8 km in the central planes of the DSR. The thickness of the pre-rift sediments reaches 10-12 km in the northern and southern planes and exceeds 15 km in the central planes of the DSR. The planes of the 3D models show that the depth to the crystalline basement under the eastern shoulders of the DSR is shallower than those beneath the western shoulders. It is about 3-5 km beneath the eastern shoulders and 7-9 km under the western shoulder of the DSR. The gravity anomaly maps of residual and first derivative gravity delineate the subsurface basins of widely varying size, shape, and depth along the Rift Valley. The basins are created by the combination of the lateral motion along a right-tending step over and normal faulting along the opposite sides. Al Bakura basin occupies the upper Jordanian River valley and extends into the southern Tiberias Lake. Bet Shean basin to the south of Al Bakura basin plunges asymmetrically toward the east. The Damia basin, comprising the central Jordan Valley and Jericho areas to the north of the Dead Sea is shallow basin (~600-800m deep). The Lisan basin is the deepest basin in the Rift. The 3D gravity models indicate a maximum of ~12 km of basin fill. Three basins are found in Wadi Araba area, Gharandal, Timna (Qa'-Taba) and Aqaba (Elat) basin. The three basins become successively wider and deeper to the south. The three regional gravity long E-W profiles (225 km) from the Mediterranean Sea crossing the Rift Valley to the east to the Saudi Arabia borders, show the positive correlation between topography and free air anomaly and strong negative Bouguer anomaly under the central part of the Dead Sea Basin (DSB) and normal regional Bouguer anomaly outside of the DSB in the transform valley. Depth to the top of the bedrock in the under ground of Jordan was calculated from potential field data. The basement crops out in the south west of Jordan and becomes deeper to northwards and eastwards to be about ~ 8 km below ground surface in the Risha area.
The natural cyclical development of palsas makes it difficult to use visible signs of decay as reference points for environmental change. Thus, to determine the actual development stage of a palsa, investigations of the internal structure are crucial. Our study presents 2‐D and 3‐D electrical resistivity imaging (ERI) and 2‐D ground‐penetrating radar (GPR) results, measurements of surface and subsurface temperatures, and of the soil matric potential from Orravatnsrústir Palsa Site in Central Iceland. By a joint interpretation of the results, we deduce the internal structure (i.e., thickness of thaw zone and permafrost, ice/water content) of five palsas of different size and shape. The results differentiate between initial and mature development stages and show that palsas of different development stages can exist in close proximity. While internal characteristics indicate undisturbed development of four palsas, one palsa shows indications of environmental change. Our study shows the value of the multimethod geophysical approach and introduces measurements of the soil matric potential as a promising method to assess the current state of the subsurface.
Via providing various ecosystem services, the old-growth Hyrcanian forests play a crucial role in the environment and anthropogenic aspects of Iran and beyond. The amount of growing stock volume (GSV) is a forest biophysical parameter with great importance in issues like economy, environmental protection, and adaptation to climate change. Thus, accurate and unbiased estimation of GSV is also crucial to be pursued across the Hyrcanian. Our goal was to investigate the potential of ALOS-2 and Sentinel-1's polarimetric features in combination with Sentinel-2 multi-spectral features for the GSV estimation in a portion of heterogeneously-structured and mountainous Hyrcanian forests. We used five different kernels by the support vector regression (nu-SVR) for the GSV estimation. Because each kernel differently models the parameters, we separately selected features for each kernel by a binary genetic algorithm (GA). We simultaneously optimized R\(^2\) and RMSE in a suggested GA fitness function. We calculated R\(^2\), RMSE to evaluate the models. We additionally calculated the standard deviation of validation metrics to estimate the model's stability. Also for models over-fitting or under-fitting analysis, we used mean difference (MD) index. The results suggested the use of polynomial kernel as the final model. Despite multiple methodical challenges raised from the composition and structure of the study site, we conclude that the combined use of polarimetric features (both dual and full) with spectral bands and indices can improve the GSV estimation over mixed broadleaf forests. This was partially supported by the use of proposed evaluation criterion within the GA, which helped to avoid the curse of dimensionality for the applied SVR and lowest over estimation or under estimation.
Environmental interlinked problems such as human-induced land cover change, water scarcity, loss in soil fertility, and anthropogenic climate change are expected to affect the viability of agriculture and increase food insecurity in many developing countries. Climate change is certainly the most serious of these challenges for the twenty-first century. The poorest regions of the world – tropical West Africa included – are the most vulnerable due to their high dependence on climate and weather sensitive activities such as agriculture, and the widespread poverty that limits the institutional and economic capacities to adapt to the new stresses brought about by climate change. Climate change is already acting negatively on the poor smallholders of tropical West Africa whose livelihoods dependent mainly on rain-fed agriculture that remains the cornerstone of the economy in the region. Adaptation of the agricultural systems to climate change effects is, therefore, crucial to secure the livelihoods of these rural communities. Since information is a key for decision-making, it is important to provide well-founded information on the magnitude of the impacts in order to design appropriate and sustainable adaptation strategies.
Considering the case of agricultural production in the Republic of Benin, this study aims at using large-scale climatic predictors to assess the potential impacts of past and future climate change on agricultural productivity at a country scale in West Africa. Climate signals from large-scale circulation were used because state-of-the art regional climate models (RCM) still do not perfectly resolve synoptic and mesoscale convective processes. It was hypothesised that in rain-fed systems with low investments in agricultural inputs, yield variations are widely governed by climatic factors. Starting with pineapple, a perennial fruit crops, the study further considered some annual crops such as cotton in the group of fibre crops, maize, sorghum and rice in the group of cereals, cowpeas and groundnuts belonging to the legume crops, and cassava and yams which are root and tuber crops. Thus the selected crops represented the three known groups of photosynthetic pathways (i.e. CAM, C3, and C4 plants).
In the study, use was made of the historical agricultural yield statistics for the Republic of Benin, observed precipitation and mean near-surface air temperature data from the Climatic Research Unit (CRU TS 3.1) and the corresponding variables simulated by the regional climate model (RCM) REMO. REMO RCM was driven at its boundaries by the global climate model ECHAM 5. Simulations with different greenhouse gas concentrations (SRES-A1B and B1 emission scenarios) and transient land cover change scenarios for present-day and future conditions were considered. The CRU data were submitted to empirical orthogonal functions analysis over the north hemispheric part of Africa to obtain large-scale observed climate predictors and associated consistent variability modes. REMO RCM data for the same region were projected on the derived climate patterns to get simulated climate predictors. By means of cross-validated Model Output Statistics (MOS) approach combined with Bayesian model averaging (BMA) techniques, the observed climate predictors and the crop predictand were further on used to derive robust statistical relationships. The robust statistical crop models perform well with high goodness-of-fit coefficients (e.g. for all combined crop models: 0.49 ≤ R2 ≤ 0.99; 0.28 ≤ Brier-Skill-Score ≤ 0.90).
Provided that REMO RCM captures the main features of the real African climate system and thus is able to reproduce its inter-annual variability, the time-independent statistical transfer functions were then used to translate future climate change signal from the simulated climate predictors into attainable crop yields/crop yield changes. The results confirm that precipitation and air temperature governed agricultural production in Benin in general, and particularly, pineapple yield variations are mainly influenced by temperature. Furthermore, the projected yield changes under future anthropogenic climate change during the first-half of the 21st century amount up to -12.5% for both maize and groundnuts, and -11%, -29%, -33% for pineapple, cassava, and cowpeas respectively. Meanwhile yield gain of up to +10% for sorghum and yams, +24% for cotton, and +39% for rice are expected. Over the time period 2001 – 2050, on average the future yield changes range between -3% and -13% under REMO SRES–B1 (GHG)+LCC, -2% and -11% under REMO SRES–A1B (GHG only),and -3% and -14% under REMO SRES–A1B (GHG)+LCC for pineapple, maize, sorghum, groundnuts, cowpeas and cassava. In the meantime for yams, cotton and rice, the average yield gains lie in interval of about +2% to +7% under REMO SRES–B1 (GHG)+LCC, +0.1% and +12% under REMO SRES–A1B (GHG only), and +3% and +10% under REMO SRES–A1B (GHG)+LCC. For sorghum, although the long-term average future yield depicts a reduction there are tendencies towards increasing yields in the future. The results also reveal that the increases in mean air temperature more than the changes in precipitation patterns are responsible for the projected yield changes. As well the results suggest that the reductions in pineapple yields cannot be attributed to the land cover/land use changes across sub-Saharan Africa. The production of groundnuts and in particular yams and cotton will profit from the on-going land use/land cover changes while the other crops will face detrimental effects.
Henceforth, policymakers should take effective measures to limit the on-going land degradation processes and all other anthropogenic actions responsible for temperature increase. Biotechnological improvement of the cultivated crop varieties towards development of set of seed varieties adapted to hotter and dry conditions should be included in the breeding pipeline programs. Amongst other solutions, application of appropriate climate-smart agricultural practices and conservation agriculture are also required to offset the negative impacts of climate change in agriculture.
This thesis on the “Impacts of extreme hydro-meteorological events on electricity generation and possible adaptation measures – a GIS-based approach for corporate risk management and enhanced climate mitigation concepts in Germany” presents an identification of hydro-meteorological extreme events in Germany and their effects on electricity generating units, i.e. on conventional thermal and nuclear power plants as well as on installations of the renewable energies of hydropower, wind energy and photovoltaic installations. In addition, adaptation measures and strategies are named that help power plant operators to prepare for a changing climate. Due to the different requirements of large facility operators and local planners and owners of renewable energies, the work contains the two approaches of corporate risk management and climate mitigation concepts. A changing climate not only consists of a shift in mean values of weather parameters such as global and regional air temperature and precipitation, but may also result in more frequent and more severe single events such as extreme precipitation, tornadoes and thunderstorms. In two case studies, these findings are implemented into an adjusted general risk management structure. This is enhanced by the use of Geographical Information Systems (GIS) to accomplish a localisation of events and infrastructure. The first example gives insight into the consequences of ice throw from wind turbines and how climate mitigation concepts can act as a framework for an adapted, sustainable energy planning. The second example on the other hand highlights a GIS-based flood risk management for thermal power plants and the benefits of an adjusted corporate risk management cycle. The described approach leads to an integrated management of extreme hydro-meteorological events at power plant site respectively district level by combining two cycles of site-related and local planning in addition to GIS-based analyses. This is demonstrated as an example by the comparison of two districts in Germany. The practical outcome is a comprehensive support for decision-making processes.
The Kunduz River is one of the main tributaries of the Amu Darya Basin in North Afghanistan. Many communities live in the Kunduz River Basin (KRB), and its water resources have been the basis of their livelihoods for many generations. This study investigates climate change impacts on the KRB catchment. Rare station data are, for the first time, used to analyze systematic trends in temperature, precipitation, and river discharge over the past few decades, while using Mann–Kendall and Theil–Sen trend statistics. The trends show that the hydrology of the basin changed significantly over the last decades. A comparison of landcover data of the river basin from 1992 and 2019 shows significant changes that have additional impact on the basin hydrology, which are used to interpret the trend analysis. There is considerable uncertainty due to the data scarcity and gaps in the data, but all results indicate a strong tendency towards drier conditions. An extreme warming trend, partly above 2 °C since the 1960s in combination with a dramatic precipitation decrease by more than −30% lead to a strong decrease in river discharge. The increasing glacier melt compensates the decreases and leads to an increase in runoff only in the highland parts of the upper catchment. The reduction of water availability and the additional stress on the land leads to a strong increase of barren land and a reduction of vegetation cover. The detected trends and changes in the basin hydrology demand an active management of the already scarce water resources in order to sustain water supply for agriculture and ecosystems in the KRB.
This study has focused on hydrogeological and hydrochemical settings of the Northern Namibian Kalahari Catchment which is the Namibian part of the Makgadikgadi-Kalahari-Catchment. Recharge has been the subject of process-understanding, quantification and regionalisation. Within the semiarid study area a bimodal surface constitution is prominent: hardrocks areas allow for fast infiltration along karsts and joints, whereas areas covered by unconsolidated sediments receive minor diffuse recharge and locally some preferred flow path recharge develops along shrinkage cracks and rootlets. Five substratum classes have been soil physically studied: Pans and vleis, brown to red soils, dune sand, soil with an aeolian influence, and calcrete. Aeolian sands are most promising for the development of direct diffuse recharge. Recharge by preferred flow might occur in all soil classes either due to joints in calcrete or structures and rootlets in soils. All soil classes contribute to indirect recharge because even the dune sand allows, albeit very locally, the generation of runoff. The occurrences of recharge through the unconsolidated soil and the hardrocks have been confirmed by hydrograph interpretation and by a study of hydrochemical data which identified groundwater of flood water and flood water after soil passage composition. Other prominent hydrochemical processes in the Kalahari are associated with the carbonate-equilibrium-system, mixing with highly mineralised water that is either sulphate (central area) or chloride dominated (fringe area) and development of sodium hydrogencarbonate water types. The latter is mostly generated by feldspar weathering. Variations of the hydrochemical compositions were observed for shallow groundwaters. They do not only reflect the recharge amount but also the recharge conditions, e.g. a wetter year is allowing more vegetation which increases the hydrogencarbonate content. Inverse determination of recharge by the chloride mass balance method gives recharge amounts between 0.2 and locally more than 100 mm/a. The least favoured recharge conditions are found for Kalahari covered areas, the largest amount occurs in the Otavi area. The distribution of recharge areas within the catchment is rather complex and regionalisation of recharge for the entire catchment was done by a forward approach using satellite images and by an inverse approach using hydrochemical data. From the inverse hydrochemical approach a basin-wide balanced recharge amount of 1.39 mm/a is achieved. The forward approach gave a basin-wide figure of 0.88 (minimum assumption) to 4.53 mm/a (maximum assumption). A simplistic groundwater flow model confirmed the results from the minimum recharge regionalisation by satellite images and the result from the hydrochemical approach. Altogether a mutually verified basin-wide recharge figure of ca. 1 mm/a turns out.