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The seasonal snow cover in the European Alps plays a crucial role in the region's climate, ecology, and economy. It affects the local climate through its high albedo, protects permafrost, provides habitats, and acts as a water reservoir that feeds European rivers. However, these functions are threatened by climate change. Analyzing snow cover dynamics is essential to predict future developments and assess related ecological and economic impacts.
This study explores the potential of long Earth Observation (EO) time series for modeling and predicting the snow line elevation (SLE) in the Alps. Based on approximately 15,000 Landsat satellite images, SLE time series were generated for the years 1985 to 2022. Various univariate forecasting models were evaluated, with the best results achieved by Random Forests, Telescope, and Seasonal ARIMA. A newly developed approach combines the best models into a robust ensemble, achieving an average Nash-Sutcliffe efficiency (NSE) of 0.8 in catchments with strong seasonal signals.
Forecasts for 2030 indicate significant upward shifts in the SLE, particularly in the Western and Southern Alps. Given the variability in results, a multivariate modeling approach using climate variables is recommended to improve prediction accuracy. This study lays the groundwork for future models that could potentially project SLE dynamics through the end of the 21st century under various climate scenarios, which is highly relevant for climate policy in the Alpine region.
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.
Die mit dem Klimawandel einhergehenden Umweltveränderungen, wie steigende Temperaturen, Abnahme der Sommer- und Zunahme der Winterniederschläge, häufigere und längere Trockenperioden, zunehmende Starkniederschläge, Stürme und Hitzewellen betreffen besonders den Bodenwasserhaushalt in seiner zentralen Regelungsfunktion für den Landschaftswasserhaushalt. Von der Wasserverfügbarkeit im Boden hängen zu einem sehr hohen Grad auch die Erträge der Land- und Forstwirtschaft ab. Eine besonders große Bedeutung kommt dabei der Wasserspeicherkapazität der Böden zu, da während einer Trockenphase die effektiven Niederschläge den Wasserbedarf der Pflanzen nicht decken können und das bereits gespeicherte Bodenwasser das Überleben der Pflanzen sicherstellen kann. Für die land- und forstwirtschaftlichen Akteure sind in diesem Kontext quantitative und qualitative Aussagen zu den Auswirkungen des Klimawandels auf den Boden essenziell, um die notwendigen Anpassungsmaßnahmen für ihre Betriebe treffen zu können.
Zielsetzungen der vorliegenden Arbeit bestehen darin, die Dynamik der Bodenfeuchte in unterfränkischen Böden besser zu verstehen, die Datenlage zum Verlauf der Bodenfeuchte zu verbessern und die Auswirkungen von prognostizierten klimatischen Parametern abschätzen zu können. Hierzu wurden an sechs für ihre jeweiligen Naturräume und hinsichtlich ihrer anthropogenen Nutzung charakteristischen Standorten meteorologisch-bodenhydrologische Messstationen installiert. Die Messstationen befinden sich in einem Rigosol auf Buntsandstein in einem Weinberg bei Bürgstadt sowie auf einer Parabraunerde im Lössgebiet bei Herchsheim unter Ackernutzung. Am Übergang von Muschelkalk in Keuper befinden sich die Stationen in Obbach, wo eine Braunerde unter Ackernutzung vorliegt und im Forst des Universitätswalds Sailershausen werden die Untersuchungen in einer Braunerde-Terra fusca durchgeführt. Im Forst befinden sich auch die Stationen in Oberrimbach mit Braunerden aus Sandsteinkeuper und in Willmars mit Braunerden aus Buntsandstein. Der Beobachtungszeitraum dieser Arbeit reicht von Juli 2018 bis November 2022. In diesen Zeitraum fiel die dreijährige Dürre von 2018 bis 2020, das Jahr 2021 mit einem durchschnittlichen Witterungsverlauf und das Dürrejahr 2022.
Das Langzeitmonitoring wurde von umfangreichen Gelände- und Laboranalysen der grundlegenden bodenkundlichen Parameter der Bodenprofile und der Standorte begleitet. Die bodengeographischen-geomorphologischen Standortanalysen bilden zusammen mit den qualitativen Auswertungen der Bodenfeuchtezeitreihen die Grundlage für Einschätzungen zu den Auswirkungen des Klimawandels auf den Bodenwasserhaushalt. Verlässliche Aussagen zum Bodenwasserhaushalt können nur auf Grundlage von zeitlich und räumlich hoch aufgelösten Daten getroffen werden. Bodenfeuchtezeitreihen zusammen mit den bodenphysikalischen Daten lagen in dieser Datenqualität für Unterfranken bisher nur sehr vereinzelt vor.
Die vorliegenden Ergebnisse zeigen, dass die untersuchten Böden entsprechend den jeweiligen naturräumlichen Gegebenheiten sehr unterschiedliche bodenhydrologische Eigenschaften aufweisen. Während langer Trockenphasen können beispielsweise die Parabraunerden am Standort Herchsheim wegen ihrer höheren Wasserspeicherkapazität die Pflanzen länger mit Wasser versorgen als die sandigen Braunerden am Standort Oberrimbach. Die Bodenfeuchteregime im Beobachtungszeitraum waren sehr stark vom Witterungsverlauf einzelner Jahre abhängig. Das Bodenfeuchteregime bei einem durchschnittlichen Witterungsverlauf wie in 2021 zeichnet sich durch eine langsame Abnahme der Bodenfeuchte ab Beginn der Vegetationsperiode im Frühjahr aus. Regelmäßige Niederschläge im Frühjahr füllen den oberflächennahen Bodenwasserspeicher immer wieder auf und sichern den Bodenwasservorrat in der Tiefe bis in den Hochsommer. Im Hochsommer können Pflanzen dann während der Trockenphasen ihren Wasserbedarf aus den tieferen Horizonten decken. Im Gegensatz dazu nimmt die Bodenfeuchte in Dürrejahren wie 2018 bis 2020 oder 2022 bereits im Frühjahr bis in die untersten Horizonte stark ab. Die nutzbare Feldkapazität ist zum Teil schon im Juni weitgehend ausgeschöpft, womit für spätere Trockenphasen kein Bodenwasser mehr zur Verfügung steht. Die Herbst- und Winterniederschläge sättigen den Bodenwasservorrat wieder bis zur Feldkapazität auf. Bei tiefreichender Erschöpfung des Bodenwassers wurde die Feldkapazität erst im Januar oder Februar erreicht.
Im Zuge der land- und forstwirtschaftlichen Nutzung ist eine gute Datenlage zu den bodenkundlichen und standörtlichen Gegebenheiten für klimaadaptierte Anpassungsstrategien essentiell. Wichtige Zielsetzungen bestehen grundsätzlich in der Erhaltung der Bodenfunktionen, in der Verbesserung der Infiltrationskapazität und Wasserspeicherkapazität. Hier kommt dem Boden als interaktive Austauschfläche zwischen den Sphären und damit dem Bodenschutz eine zentrale Bedeutung zu. Die in Zukunft erwarteten klimatischen Bedingungen stellen an jeden Boden andere Herausforderungen, welchen mit standörtlich abgestimmten Bodenschutzmaßnahmen begegnet werden kann.
Permafrost degradation is observed all over the world as a consequence of climate change and the associated Arctic amplification, which has severe implications for the environment. Landslides, increased rates of surface deformation, rising likelihood of infrastructure damage, amplified coastal erosion rates, and the potential turnover of permafrost from a carbon sink to a carbon source are thereby exemplary implications linked to the thawing of frozen ground material. In this context, satellite earth observation is a potent tool for the identification and continuous monitoring of relevant processes and features on a cheap, long-term, spatially explicit, and operational basis as well as up to a circumpolar scale.
A total of 325 articles published in 30 different international journals during the past two decades were investigated on the basis of studied environmental foci, remote sensing platforms, sensor combinations, applied spatio-temporal resolutions, and study locations in an extensive review on past achievements, current trends, as well as future potentials and challenges of satellite earth observation for permafrost related analyses. The development of analysed environmental subjects, utilized sensors and platforms, and the number of annually published articles over time are addressed in detail. Studies linked to atmospheric features and processes, such as the release of greenhouse gas emissions, appear to be strongly under-represented. Investigations on the spatial distribution of study locations revealed distinct study clusters across the Arctic. At the same time, large sections of the continuous permafrost domain are only poorly covered and remain to be investigated in detail. A general trend towards increasing attention in satellite earth observation of permafrost and related processes and features was observed. The overall amount of published articles hereby more than doubled since the year 2015. New sources of satellite data, such as the Sentinel satellites and the Methane Remote Sensing LiDAR Mission (Merlin), as well as novel methodological approaches, such as data fusion and deep learning, will thereby likely improve our understanding of the thermal state and distribution of permafrost, and the effects of its degradation. Furthermore, cloud-based big data processing platforms (e.g. Google Earth Engine (GEE)) will further enable sophisticated and long-term analyses on increasingly larger scales and at high spatial resolutions.
In this thesis, a specific focus was put on Arctic permafrost coasts, which feature increasing vulnerability to environmental parameters, such as the thawing of frozen ground, and are therefore associated with amplified erosion rates. In particular, a novel monitoring framework for quantifying Arctic coastal erosion rates within the permafrost domain at high spatial resolution and on a circum-Arctic scale is presented within this thesis. Challenging illumination conditions and frequent cloud cover restrict the applicability of optical satellite imagery in Arctic regions. In order to overcome these limitations, Synthetic Aperture RADAR (SAR) data derived from Sentinel-1 (S1), which is largely independent from sun illumination and weather conditions, was utilized. Annual SAR composites covering the months June–September were combined with a Deep Learning (DL) framework and a Change Vector Analysis (CVA) approach to generate both a high-quality and circum-Arctic coastline product as well as a coastal change product that highlights areas of erosion and build-up. Annual composites in the form of standard deviation (sd) and median backscatter were computed and used as inputs for both the DL framework and the CVA coastal change quantification. The final DL-based coastline product covered a total of 161,600 km of Arctic coastline and featured a median accuracy of ±6.3 m to the manually digitized reference data. Annual coastal change quantification between 2017–2021 indicated erosion rates of up to 67 m per year for some areas based on 400 m coastal segments. In total, 12.24% of the investigated coastline featured an average erosion rate of 3.8 m per year, which corresponds to 17.83 km2 of annually eroded land area. Multiple quality layers associated to both products, the generated DL-coastline and the coastal change rates, are provided on a pixel basis to further assess the accuracy and applicability of the proposed data, methods, and products.
Lastly, the extracted circum-Arctic erosion rates were utilized as a basis in an experimental framework for estimating the amount of permafrost and carbon loss as a result of eroding permafrost coastlines. Information on permafrost fraction, Active Layer Thickness (ALT), soil carbon content, and surface elevation were thereby combined with the aforementioned erosion rates. While the proposed experimental framework provides a valuable outline for quantifying the volume loss of frozen ground and carbon release, extensive validation of the utilized environmental products and resulting volume loss numbers based on 200 m segments are necessary. Furthermore, data of higher spatial resolution and information of carbon content for deeper soil depths are required for more accurate estimates.
Accurate crop monitoring in response to climate change at a regional or field scale plays a significant role in developing agricultural policies, improving food security, forecasting, and analysing global trade trends. Climate change is expected to significantly impact agriculture, with shifts in temperature, precipitation patterns, and extreme weather events negatively affecting crop yields, soil fertility, water availability, biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable information combined with crop growth models (CGMs) for yield assessment by monitoring crop development, detecting crop changes, and assessing the impact of climate change on crop yields. This dissertation aims to investigate the potential of RS data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR) for the Free State of Bavaria (70,550 km2), Germany. The first chapter of the dissertation describes the reasons favouring the importance of accurate crop yield predictions for achieving sustainability in agriculture. Chapter second explores the accuracy assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more suitable for agricultural monitoring than the other synthetic products fused. Chapter third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter 2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal (8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light use efficiency approach (LUE)) for accurate crop yield estimations of both crop types. Chapter third observes that the observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately measuring the yield of WW and OSR. The chapter investigates that the simple light use efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Chapter four researches the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for WW and OSR using the LUE model for Bavaria from 2001 to 2019. The chapter states the high positive correlation coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter analyses the impact of climate variables on crop yield predictions by observing an increase in R2 (0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when the climate effect is included in the model. The fifth chapter suggests that the coupling of the LUE model to the random forest (RF) model can further reduce the relative root mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. The same chapter concludes that satellite-based crop biomass, solar radiation, and temperature are the most influential variables in the yield prediction of both crop types. Chapter six attempts to discuss both pros and cons of RS technology while analysing the impact of land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that the modelled biomass of both crops is positively impacted by land use diversity to the radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The chapter also discusses the future implications by stating that including some dependent factors (such as the management practices used, soil health, pest management, and pollinators) could improve the relationship of RS-modelled crop yields with biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving accurate crop yield predictions for precision farming. In addition, the chapter highlights the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher crop yield accuracies.
Accurate crop monitoring in response to climate change at a regional or field scale
plays a significant role in developing agricultural policies, improving food security,
forecasting, and analysing global trade trends. Climate change is expected to
significantly impact agriculture, with shifts in temperature, precipitation patterns, and
extreme weather events negatively affecting crop yields, soil fertility, water availability,
biodiversity, and crop growing conditions. Remote sensing (RS) can provide valuable
information combined with crop growth models (CGMs) for yield assessment by
monitoring crop development, detecting crop changes, and assessing the impact of
climate change on crop yields. This dissertation aims to investigate the potential of RS
data on modelling long-term crop yields of winter wheat (WW) and oil seed rape (OSR)
for the Free State of Bavaria (70,550 km2
), Germany. The first chapter of the dissertation
describes the reasons favouring the importance of accurate crop yield predictions for
achieving sustainability in agriculture. Chapter second explores the accuracy
assessment of the synthetic RS data by fusing NDVIs of two high spatial resolution data
(high pair) (Landsat (30 m, 16-days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low
spatial resolution data (low pair) (MOD13Q1 (250 m, 16-days), MCD43A4 (500 m, one
day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, 8-days)) using the spatial
and temporal adaptive reflectance fusion model (STARFM), which fills regions' cloud
or shadow gaps without losing spatial information. The chapter finds that both L-MOD13Q1 (R2 = 0.62, RMSE = 0.11) and S-MOD13Q1 (R2 = 0.68, RMSE = 0.13) are more
suitable for agricultural monitoring than the other synthetic products fused. Chapter
third explores the ability of the synthetic spatiotemporal datasets (obtained in chapter
2) to accurately map and monitor crop yields of WW and OSR at a regional scale. The
chapter investigates and discusses the optimal spatial (10 m, 30 m, or 250 m), temporal
(8 or 16-day) and CGMs (World Food Studies (WOFOST), and the semi-empiric light
use efficiency approach (LUE)) for accurate crop yield estimations of both crop types.
Chapter third observes that the observations of high temporal resolution (8-day)
products of both S-MOD13Q1 and L-MOD13Q1 play a significant role in accurately
measuring the yield of WW and OSR. The chapter investigates that the simple light use
efficiency (LUE) model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) that required fewer input parameters to simulate crop yield is highly accurate, reliable, and more
precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher
input parameters. Chapter four researches the relationship of spatiotemporal fusion
modelling using STRAFM on crop yield prediction for WW and OSR using the LUE
model for Bavaria from 2001 to 2019. The chapter states the high positive correlation
coefficient (R) = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and
modelled yield accuracy for WW and OSR from 2001 to 2019, respectively. The chapter
analyses the impact of climate variables on crop yield predictions by observing an
increase in R2
(0.79 (WW)/0.86 (OSR)) and a decrease in RMSE (4.51/2.57 dt/ha) when
the climate effect is included in the model. The fifth chapter suggests that the coupling
of the LUE model to the random forest (RF) model can further reduce the relative root
mean square error (RRMSE) from -8% (WW) and -1.6% (OSR) and increase the R2 by
14.3% (for both WW and OSR), compared to results just relying on LUE. The same
chapter concludes that satellite-based crop biomass, solar radiation, and temperature
are the most influential variables in the yield prediction of both crop types. Chapter six
attempts to discuss both pros and cons of RS technology while analysing the impact of
land use diversity on crop-modelled biomass of WW and OSR. The chapter finds that
the modelled biomass of both crops is positively impacted by land use diversity to the
radius of 450 (Shannon Diversity Index ~0.75) and 1050 m (~0.75), respectively. The
chapter also discusses the future implications by stating that including some dependent
factors (such as the management practices used, soil health, pest management, and
pollinators) could improve the relationship of RS-modelled crop yields with
biodiversity. Lastly, chapter seven discusses testing the scope of new sensors such as
unmanned aerial vehicles, hyperspectral sensors, or Sentinel-1 SAR in RS for achieving
accurate crop yield predictions for precision farming. In addition, the chapter highlights
the significance of artificial intelligence (AI) or deep learning (DL) in obtaining higher
crop yield accuracies.
Grasslands shape many landscapes of the earth as they cover about one-third of its surface. They are home and provide livelihood for billions of people and are mainly used as source of forage for animals. However, grasslands fulfill many additional ecosystem functions next to fodder production, such as storage of carbon, water filtration, provision of habitats and cultural values. They play a role in climate change (mitigation) and in preserving biodiversity and ecosystem functions on a global scale. The degree to what these ecosystem functions are present within grassland ecosystems is largely determined by the management. Individual management practices and the use intensity influence the species composition as well as functions, like carbon storage, while higher use intensities (e.g. high mowing frequencies) usually show a negative impact. Especially in Central European countries, like in Germany, the determining influence of grassland management on its physiognomy and ecosystem functions leads to a large variability and small-scale alternations of grassland parcels. Large-scale information on the management and use intensity of grasslands is not available. Consequently, estimations of grassland ecosystem functions are challenging which, however, would be required for large-scale assessments of the status of grassland ecosystems and optimized management plans for the future. The topic of this thesis tackles this gap by investigating the major grassland management practice in Germany, which is mowing, for multiple years, in high spatial resolution
and on a national scale.
Earth Observation (EO) has the advantage of providing information of the earth’s surface on multi-temporal time steps. An extensive literature review on the use of EO for grassland management and production analyses, which was part of this thesis, showed that in particular research on grasslands consisting of small parcels with a large variety of management and use intensity, like common in Central Europe, is underrepresented. Especially
the launch of the Sentinel satellites in the recent past now enables the analyses of such grasslands due to their high spatial and temporal resolution. The literature review specifically on the investigation of grassland mowing events revealed that most previous studies focused on small study areas, were exploratory, only used one sensor type and/or lacked a reference data set with a complete range of management options.
Within this thesis a novel framework to detect grassland mowing events over large areas is presented which was applied and validated for the entire area of Germany for multiple years (2018–2021). The potential of both sensor types, optical (Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel-1) was investigated regarding grassland mowing event detection. Eight EO parameters were investigated, namely the Enhanced Vegetation Index (EVI), the backscatter intensity and the interferometric (InSAR) temporal coherence for both available polarization modes (VV and VH), and the polarimetric (PolSAR) decomposition parameters Entropy, K0 and K1. An extensive reference data set was generated based on daily images of webcams distributed in Germany which resulted in mowing information
for grasslands with the entire possible range of mowing frequencies – from one to six in Germany – and in 1475 reference mowing events for the four years of interest.
For the first time a observation-driven mowing detection approach including data from Sentinel-2 and Sentinel-1 and combining the two was developed, applied and validated on large scale. Based on a subset of the reference data (13 grassland parcels with 44 mowing events) from 2019 the EO parameters were investigated and the detection algorithm
developed and parameterized. This analysis showed that a threshold-based change detection approach based on EVI captured grassland mowing events best, which only failed during periods of clouds. All SAR-based parameters showed a less consistent behavior to mowing events, with PolSAR Entropy and InSAR Coherence VH, however, revealing the
highest potential among them. A second, combined approach based on EVI and a SARbased parameter was developed and tested for PolSAR Entropy and InSAR VH. To avoid additional false positive detections during periods in which mowing events are anyhow reliably detected using optical data, the SAR-based mowing detection was only initiated
during long gaps within the optical time series (< 25 days). Application and validation of
these approaches in a focus region revealed that only using EVI leads to the highest accuracies (F1-Score = 0.65) as combining this approach with SAR-based detection led to a strong increase in falsely detected mowing events resulting in a decrease of accuracies (EVI + PolSAR ENT F1-Score = 0.61; EVI + InSAR COH F1-Score = 0.61).
The mowing detection algorithm based on EVI was applied for the entire area of Germany for the years 2018-2021. It was revealed that the largest share of grasslands with high mowing frequencies (at least four mowing events) can be found in southern/south-eastern Germany. Extensively used grassland (mown up to two times) is distributed within the entire country with larger shares in the center and north-eastern parts of Germany. These patterns stay constant in general, but small fluctuations between the years are visible. Early mown grasslands can be found in southern/south-eastern Germany – in line with high mowing frequency areas – but also in central-western parts. The years 2019 and 2020 revealed higher accuracies based on the 1475 mowing events of the multi-annual validation data set
(F1-Scores of 0.64 and 0.63), 2018 and 2021 lower ones (F1-Score of 0.52 and 0.50).
Based on this new, unprecedented data set, potential influencing factors on the mowing dynamics were investigated. Therefore, climate, topography, soil data and information on conservation schemes were related to mowing dynamics for the year 2020, which showed a high number of valid observations and detection accuracy. It was revealed that there are no strong linear relationships between the mowing frequency or the timing of the first mowing event and the investigated variables. However, it was found that for intensive grassland usage certain climatic and topographic conditions have to be fulfilled, while extensive grasslands appear on the entire spectrum of these variables. Further, higher mowing frequencies occur on soils with influence of ground water and lower mowing frequencies in protected areas. These results show the complex interplay between grassland mowing dynamics and external influences and highlight the challenges of policies aiming to protect grassland ecosystem functions and their need to be adapted to regional circumstances.
Die städtische Umwelt ist in steter Veränderung, vor allem durch den Bau, aber auch durch die Zerstörung von städtischen Elementen. Die formelle Entwicklung ist ein Prozess mit langen Planungszeiträumen und die bebaute Landschaft wirkt daher statisch. Dagegen unterliegen informelle oder spontane Siedlungen aufgrund ihrer stets unvollendeten städtischen Form einer hohen Dynamik – so wird in der Literatur berichtet. Allerdings sind Dynamik und die morphologischen Merkmale der physischen Transformation in solchen Siedlungen, die städtische Armut morphologisch repräsentieren, auf globaler Ebene bisher kaum mit einer konsistenten Datengrundlage empirisch untersucht worden. Hier setzt die vorliegende Arbeit an. Unter der Annahme, dass die erforschte zeitliche Dynamik in Europa geringer ausfällt, stellt sich die generelle Frage nach einer katalogisierten Erfassung physischer Wohnformen von Armut speziell in Europa. Denn Wohnformen der Armut werden oft ausschließlich mit dem ‚Globalen Süden‘ assoziiert, insbesondere durch die Darstellung von Slums. Tatsächlich ist Europa sogar die Wiege der Begriffe ‚Slum‘ und ‚Ghetto‘, die vor Jahrhunderten zur Beschreibung von Missständen und Unterdrückung auftauchten. Bis heute weist dieser facettenreiche Kontinent eine enorme Vielfalt an physischen Wohnformen der Armut auf, die ihre Wurzeln in unterschiedlichen Politiken, Kulturen, Geschichten und Lebensstilen haben. Um über diese genannten Aspekte Aufschluss zu erlangen, bedarf es u.a. der Bildanalyse durch Satellitenbilder. Diese Arbeit wird daher mittels Fernerkundung bzw. Erdbeobachtung (EO) sowie zusätzlicher Literaturrecherchen und einer empirischen Erhebung erstellt. Um Unsicherheiten konzeptionell und in der Erfassung offenzulegen, ist die Methode der manuellen Bildinterpretation von Armutsgebieten kritisch zu hinterfragen.
Das übergeordnete Ziel dieser Arbeit ist eine bessere Wissensbasis über Armut zu schaffen, um Maßnahmen zur Reduzierung von Armut entwickeln zu können. Die Arbeit dient dabei als eine Antwort auf die Nachhaltigkeitsziele der Vereinten Nationen. Es wird Grundlagenforschung betrieben, indem Wissenslücken in der Erdbeobachtung zu physisch-baulichen bzw. morphologischen Erscheinungen von Armut auf Gebäude-Ebene explorativ analysiert werden. Die Arbeit wird in drei Forschungsthemen bzw. Studienteile untergliedert:
Ziel des ersten Studienteils ist die globale raumzeitliche Erfassung von Dynamiken durch Anknüpfung an bisherige Kategorisierungen von Armutsgebieten. Die bisherige Wissenslücke soll gefüllt werden, indem über einen Zeitraum von etwa sieben Jahren in 16 dokumentierten Manifestationen städtischer Armut anhand von Erdbeobachtungsdaten eine zeitliche Analyse der bebauten Umwelt durchgeführt wird. Neben einer global verteilten Gebietsauswahl wird die visuelle Bildinterpretation (MVII) unter Verwendung von hochauflösenden optischen Satellitendaten genutzt. Dies geschieht in Kombination mit in-situ- und Google Street View-Bildern zur Ableitung von 3D-Stadtmodellen. Es werden physische Raumstrukturen anhand von sechs räumlichen morphologischen Variablen gemessen: Anzahl, Größe, Höhe, Ausrichtung und Dichte der Gebäude sowie Heterogenität der Bebauung. Diese ‚temporale Analyse‘ zeigt zunächst sowohl inter- als auch intra-urbane Unterschiede. Es lassen sich unterschiedliche, aber generell hohe morphologische Dynamiken zwischen den Untersuchungsgebieten finden. Dies drückt sich in vielfältiger Weise aus: von abgerissenen und rekonstruierten Gebieten bis hin zu solchen, wo Veränderungen innerhalb der gegebenen Strukturen auftreten. Geographisch gesehen resultiert in der Stichprobe eine fortgeschrittene Dynamik, insbesondere in Gebieten des Globalen Südens. Gleichzeitig lässt sich eine hohe räumliche Variabilität der morphologischen Transformationen innerhalb der untersuchten Gebiete beobachten. Trotz dieser teilweise hohen morphologischen Dynamik sind die räumlichen Muster von Gebäudefluchten, Straßen und Freiflächen überwiegend konstant. Diese ersten Ergebnisse deuten auf einen geringen Wandel in Europa hin, weshalb diese europäischen Armutsgebiete im folgenden Studienteil von Grund auf erhoben und kategorisiert werden.
Ziel des zweiten Studienteils ist die Erschaffung einer neuen Kategorisierung, speziell für das in der Wissenschaft unterrepräsentierte Europa. Die verschiedenen Formen nicht indizierter Wohnungsmorphologien werden erforscht und kategorisiert, um das bisherige globale wissenschaftliche ontologische Portfolio für Europa zu erweitern. Hinsichtlich dieses zweiten Studienteils bietet eine Literaturrecherche mit mehr als 1.000 gesichteten Artikeln die weitere Grundlage für den folgenden Fokus auf Europa. Auf der Recherche basierend werden mittels der manuellen visuellen Bildinterpretation (engl.: MVII) erneut Satellitendaten zur Erfassung der physischen Morphologien von Wohnformen genutzt. Weiterhin kommen selbst definierte geographische Indikatoren zu Lage, Struktur und formellem Status zum Einsatz. Darüber hinaus werden gesellschaftliche Hintergründe, die durch Begriffe wie ‚Ghetto‘, ‚Wohnwagenpark‘, ‚ethnische Enklave‘ oder ‚Flüchtlingslager‘ beschrieben werden, recherchiert und implementiert. Sie sollen als Erklärungsansatz für Armutsviertel in Europa dienen. Die Stichprobe der europäischen, insgesamt aber unbekannten Grundgesamtheit verdeutlicht eine große Vielfalt an physischen Formen: Es wird für Europa eine neue Kategorisierung von sechs Hauptklassen entwickelt, die von ‚einfachsten Wohnstätten‘ (z. B. Zelten) über ‚behelfsmäßige Unterkünfte ‘ (z. B. Baracken, Container) bis hin zu ‚mehrstöckigen Bauten‘ - als allgemeine Taxonomie der Wohnungsnot in Europa - reicht. Die Untersuchung zeigt verschiedene Wohnformen wie z. B. unterirdische oder mobile Typen, verfallene Wohnungen oder große Wohnsiedlungen, die die Armut im Europa des 21. Jahrhunderts widerspiegeln. Über die Wohnungsmorphologie hinaus werden diese Klassen durch die Struktur und ihren rechtlichen Status beschrieben - entweder als geplante oder als organisch-gewachsene bzw. weiterhin als formelle, informelle oder hybride (halblegale) Formen. Geographisch lassen sich diese ärmlichen Wohnformen sowohl in städtischen als auch in ländlichen Gebieten finden, mit einer Konzentration in Südeuropa. Der Hintergrund bei der Mehrheit der Morphologien betrifft Flüchtlinge, ethnische Minderheiten und sozioökonomisch benachteiligte Menschen - die ‚Unterprivilegierten‘.
Ziel des dritten Studienteils ist eine kritische Analyse der Methode. Zur Erfassung all dieser Siedlungen werden heutzutage Satellitenbilder aufgrund der Fortschritte bei den Bildklassifizierungsmethoden meist automatisch ausgewertet. Dennoch spielt die MVII noch immer eine wichtige Rolle, z.B. um Trainingsdaten für Machine-Learning-Algorithmen zu generieren oder für Validierungszwecke. In bestimmten städtischen Umgebungen jedoch, z.B. solchen mit höchster Dichte und struktureller Komplexität, fordern spektrale und textur-basierte Verflechtungen von überlappenden Dachstrukturen den menschlichen Interpreten immer noch heraus, wenn es darum geht einzelne Gebäudestrukturen zu erfassen. Die kognitive Wahrnehmung und die Erfahrung aus der realen Welt sind nach wie vor unumgänglich. Vor diesem Hintergrund zielt die Arbeit methodisch darauf ab, Unsicherheiten speziell bei der Kartierung zu quantifizieren und zu interpretieren. Kartiert werden Dachflächen als ‚Fußabdrücke‘ solcher Gebiete. Der Fokus liegt dabei auf der Übereinstimmung zwischen mehreren Bildinterpreten und welche Aspekte der Wahrnehmung und Elemente der Bildinterpretation die Kartierung beeinflussen. Um letztlich die Methode der MVII als drittes Ziel selbstkritisch zu reflektieren, werden Experimente als sogenannte ‚Unsicherheitsanalyse‘ geschaffen. Dabei digitalisieren zehn Testpersonen bzw. Probanden/Interpreten sechs komplexe Gebiete. Hierdurch werden quantitative Informationen über räumliche Variablen von Gebäuden erzielt, um systematisch die Konsistenz und Kongruenz der Ergebnisse zu überprüfen. Ein zusätzlicher Fragebogen liefert subjektive qualitative Informationen über weitere Schwierigkeiten. Da die Grundlage der hierfür bisher genutzten Kategorisierungen auf der subjektiven Bildinterpretation durch den Menschen beruht, müssen etwaige Unsicherheiten und damit Fehleranfälligkeiten offengelegt werden. Die Experimente zu dieser Unsicherheitsanalyse erfolgen quantifiziert und qualifiziert. Es lassen sich generell große Unterschiede zwischen den Kartierungsergebnissen der Probanden, aber eine hohe Konsistenz der Ergebnisse bei ein und demselben Probanden feststellen. Steigende Abweichungen korrelieren mit einer steigenden baustrukturellen (morphologischen) Komplexität. Ein hoher Grad an Individualität bei den Probanden äußert sich in Aspekten wie z.B. Zeitaufwand beim Kartieren, in-situ Vorkenntnissen oder Vorkenntnissen beim Umgang mit Geographischen Informationssystemen (GIS). Nennenswert ist hierbei, dass die jeweilige Datenquelle das Kartierungsverfahren meist beeinflusst. Mit dieser Studie soll also auch an der Stelle der angewandten Methodik eine weitere Wissenslücke gefüllt werden. Die bisherige Forschung komplexer urbaner Areale unter Nutzung der manuellen Bildinterpretation implementiert oftmals keine Unsicherheitsanalyse oder Quantifizierung von Kartierungsfehlern. Fernerkundungsstudien sollten künftig zur Validierung nicht nur zweifelsfrei auf MVII zurückgreifen können, sondern vielmehr sind Daten und Methoden notwendig, um Unsicherheiten auszuschließen.
Zusammenfassend trägt diese Arbeit zur bisher wenig erforschten morphologischen Dynamik von Armutsgebieten bei. Es werden inter- wie auch intra-urbane Unterschiede auf globaler Ebene präsentiert. Dabei sind allgemein hohe morphologische Transformationen zwischen den selektierten Gebieten festzustellen. Die Ergebnisse deuten auf einen grundlegenden Kenntnismangel in Europa hin, weshalb an dieser Stelle angeknüpft wird. Eine über Europa verteilte Stichprobe erlaubt eine neue morphologische Kategorisierung der großen Vielfalt an gefundenen physischen Formen. Die Menge an Gebieten erschließt sich in einer unbekannten Grundgesamtheit. Zur Datenaufbereitung bisheriger Analysen müssen Satellitenbilder manuell interpretiert werden. Das Verfahren birgt Unsicherheiten. Als kritische Selbstreflexion zeigt eine Reihe von Experimenten signifikante Unterschiede zwischen den Ergebnissen der Probanden auf, verdeutlicht jedoch bei ein und derselben Person Beständigkeit.
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.
With accelerating global climate change, the Antarctic Ice Sheet is exposed to increasing ice dynamic change. During 1992 and 2017, Antarctica contributed ~7.6 mm to global sea-level-rise mainly due to ocean thermal forcing along West Antarctica and atmospheric warming along the Antarctic Peninsula (API). Together, these processes caused the progressive retreat of glaciers and ice shelves and weakened their efficient buttressing force causing widespread ice flow accelerations. Holding ~91% of the global ice mass and 57.3 m of sea-level-equivalent, the Antarctic Ice Sheet is by far the largest potential contributor to future sea-level-rise.
Despite the improved understanding of Antarctic ice dynamics, the future of Antarctica remains difficult to predict with its contribution to global sea-level-rise representing the largest uncertainty in current projections. Given that recent studies point towards atmospheric warming and melt intensification to become a dominant driver for future Antarctic ice mass loss, the monitoring of supraglacial lakes and their impacts on ice dynamics is of utmost importance. In this regard, recent progress in Earth Observation provides an abundance of high-resolution optical and Synthetic Aperture Radar (SAR) satellite data at unprecedented spatial and temporal coverage and greatly supports the monitoring of the Antarctic continent where ground-based mapping efforts are difficult to perform. As an automated mapping technique for supraglacial lake extent delineation in optical and SAR satellite imagery as well as a pan-Antarctic inventory of Antarctic supraglacial lakes at high spatial and temporal resolution is entirely missing, this thesis aims to advance the understanding of Antarctic surface hydrology through exploitation of spaceborne remote sensing.
In particular, a detailed literature review on spaceborne remote sensing of Antarctic supraglacial lakes identified several research gaps including the lack of (1) an automated mapping technique for optical or SAR satellite data that is transferable in space and time, (2) high-resolution supraglacial lake extent mappings at intra-annual and inter-annual temporal resolution and (3) large-scale mapping efforts across the entire Antarctic continent. In addition, past method developments were found to be restricted to purely visual, manual or semi-automated mapping techniques hindering their application to multi-temporal satellite imagery at large-scale. In this context, the development of automated mapping techniques was mainly limited by sensor-specific characteristics including the similar appearance of supraglacial lakes and other ice sheet surface features in optical or SAR data, the varying temporal signature of supraglacial lakes throughout the year as well as effects such as speckle noise and wind roughening in SAR data or cloud coverage in optical data. To overcome these limitations, this thesis exploits methods from artificial intelligence and big data processing for development of an automated processing chain for supraglacial lake extent delineation in Sentinel-1 SAR and optical Sentinel-2 satellite imagery. The combination of both sensor types enabled to capture both surface and subsurface lakes as well as to acquire data during cloud cover or wind roughening of lakes. For Sentinel-1, a deep convolutional neural network based on residual U-Net was trained on the basis of 21,200 labeled Sentinel-1 SAR image patches covering 13 Antarctic regions. Similarly, optical Sentinel-2 data were collected over 14 Antarctic regions and used for training of a Random Forest classifier. Optical and SAR classification products were combined through decision-level fusion at bi-weekly temporal scale and unprecedented 10 m spatial resolution. Finally, the method was implemented as part of DLR’s High-Performance Computing infrastructure allowing for an automated processing of large amounts of data including all required pre- and postprocessing steps. The results of an accuracy assessment over independent test scenes highlighted the functionality of the classifiers returning accuracies of 93% and 95% for supraglacial lakes in Sentinel-1 and Sentinel-2 satellite imagery, respectively.
Exploiting the full archive of Sentinel-1 and Sentinel-2, the developed framework for the first time enabled the monitoring of seasonal characteristics of Antarctic supraglacial lakes over six major ice shelves in 2015-2021. In particular, the results for API ice shelves revealed low lake coverage during 2015-2018 and particularly high lake coverage during the 2019-2020 and 2020-2021 melting seasons. On the contrary, East Antarctic ice shelves were characterized by high lake coverage during 2016-2019 and
extremely low lake coverage during the 2020-2021 melting season. Over all six investigated ice shelves, the development of drainage systems was revealed highlighting an increased risk for ice shelf instability. Through statistical correlation analysis with climate data at varying time lags as well as annual data on Southern Hemisphere atmospheric modes, environmental drivers for meltwater ponding were revealed. In addition, the influence of the local glaciological setting was investigated through computation of annual recurrence times of lakes. Over both ice sheet regions, the complex interplay between local, regional and large-scale environmental drivers was found to control supraglacial lake formation despite local to regional discrepancies, as revealed through pixel-based correlation analysis. Local control factors included the ice surface topography, the ice shelf geometry, the presence of low-albedo features as well as a reduced firn air content and were found to exert strong control on lake distribution. On the other hand, regional controls on lake evolution were revealed to be the amount of incoming solar radiation, air temperature and wind occurrence. While foehn winds were found to dictate lake evolution over the API, katabatic winds influenced lake ponding in East Antarctica. Furthermore, the regional near-surface climate was shown to be driven by large-scale atmospheric modes and teleconnections with the tropics. Overall, the results highlight that similar driving factors control supraglacial lake formation on the API and EAIS pointing towards their transferability to other Antarctic regions.