Cette thèse met en évidence les dynamiques spatiales en cours à l'échelle des paysages sur deux bassins versants du SOOI, en se focalisant sur les dynamiques d'occupation du sol qui affectent les processus érosifs. Cette thèse s'interroge sur les processus de dégradation affectant les bassins versants et sur les données spatiales à mobiliser dans des contextes très différents tels les territoires de La Réunion et de Madagascar. Les données de télédétection constituent dans ces contextes un atout. Elles permettent d'observer de façon régulière et à différentes échelles les territoires. Deux bassins versants sont suivis, le Fiherenana (Madagascar) et la Rivière des Pluies (La Réunion). Sur le Fiherenana, l'étude de l’érosion est abordée en considérant les dynamiques d'occupation du sol entre 2001 et 2013. Les informations, extraites du traitement d'images satellite SPOT 4 et 5, montrent une dégradation importante de la couverture forestière, particulièrement dans le secteur de Ranobé (section avale). Une perte de plus de 230 km² de forêt sèche est notée en 12 ans. Les pertes de sols estimées sur la même période, selon le modèle RUSLE, montrent une augmentation de l'érodabilité en raison de l'altération du couvert végétal. Sur la Rivière des Pluies, l'étude se focalise sur l'empreinte spatiale de deux processus, l'anthropisation des planèzes et son impact sur l'imperméabilisation des sols, ainsi que celle des mouvements de masse sur les fortes pentes. Ces phénomènes sont suivis à partir d'orthophotographies de l'IGN, entre 1997 et 2012. Ces travaux apportent une réponse spatialisée et quantitative des dynamiques de dégradation en cours sur les bassins versants du SOOI. / Since the 20th centuries, the Southwest of Indian Ocean is particularly affected by anthropogenic degradations. This study aims to analyze degradations on drainage basin scale, which allows a suitable monitoring of critical areas, in the southwest of Indian Ocean. It focus on the assessment of land degradation and its causes, land cover change and erosion.The main difficulty stands in the implementation of reproducible methods and proceeds for developed and developing countries. In light of this, remote sensing data are relevant. Those data enable a regular observation of phenomena, allowing a full description of isolated lands and at different scale. This study combined, remote sensing data processing, spatial analysis and modeling to analyze degradations. Approaches include, data collection, their formatting or their preprocessing. Land cover information provided from Object-Based Image Analysis (OBIA) methodologies and Geographical Information System (GIS) authorized data conditioning and modeling. On the Fiherenana catchment, soil loss has been computed taking into account land cover changes. Those information were provided from SPOT 4 and 5 images from 2001 to 2013. Results confirmed the forest degradation especially in Ranobé, where we recorded a loss of more than 230 km² over 12 years. Lands affected by erosion process, spatialized and quantified by RUSLE model, also increased over this period. On La Rivière des Pluies, IGN’s orthophotographies, from 1997 until 2012, permitted urbans imprint analyzing. On Grand Éboulis site, vegetation monitoring revealed slumps. The study exposed spatial and quantitative results highlighting degradations dynamics on catchm

The irrigated agriculture in the Lower Chenab Canal (LCC) of Pakistan is characterized by huge water utilization both from surface and groundwater resources. Need of utilization of water from five rivers in Punjab province along with accelerated population growth has forced the construction of world’s largest irrigation network. Nevertheless, huge irrigation infrastructure, together with inappropriate drainage infrastructure, led to a build-up of shal-low groundwater levels, followed by waterlogging and secondary salinization in the soil profile. Following this era, decreased efficiency of irrigation supply system along with higher food demands had increased burdens on groundwater use, which led to a drop in groundwater levels in major parts of LCC. Previous studies in the study region revealed lacking management and maintenance of irrigation system, inflexible irrigation strategies, poor linkages between field level water supply and demands. No future strategy is present or under consideration to deal with this long time emerged groundwater situation particularly under unchanged irrigation water supply and climate change. Therefore, there is an utmost importance to assess the current profile of water use in the irrigation scheme and to device some workable strategies under future situations of land use and climate change. This study aims to investigate the spatio-temporal status of water utilization and performance of irrigation system using remote sensing data and techniques (SEBAL) in combination with other point data. Different irrigation performance indicators including equity, adequacy and reliability using evaporation fraction as main input parameter are utilized. Current profiles of land use/land cover (LULC) areas are assessed and their change detections are worked out to establish realistic future scenarios. Spatially distributed seasonal net recharge, a very important input parameter for groundwater modeling, is estimated by employing water balance approaches using spatial data from remote sensing and local norms. Such recharge results are also compared with a water table fluctuation approach. Following recharge estimation, a regional 3-D groundwater flow model using FEFLOW was set up. This model was calibrated by different approaches ranging from manual to automated pilot point (PP) approach. Sensitivity analysis was performed to see the model response against different model input parameters and to identify model regions which demand further improvements. Future climate parameters were downscaled to establish scenarios by using statistical downscaling under IPCC future emission scenarios. Modified recharge raster maps were prepared under both LULC and climate change scenarios and were fed to the groundwater model to investigate groundwater dynamics. Seasonal consumptive water use analysis revealed almost double use for kharif as compared to rabi cropping seasons with decrease from upper LCC to lower regions. Intra irrigation subdivision analysis of equity, an important irrigation performance indicator, shows less differences in water consumption in LCC. However, the other indicators (adequacy and reliability) indicate that the irrigation system is neither adequate nor reliable. Adequacy is found more pronounced during kharif as compared to rabi seasons with aver-age evaporation fraction of 0.60 and 0.67, respectively. Similarly, reliability is relatively higher in upper LCC regions as compared to lower regions. LULC classification shows that wheat and rice are major crops with least volatility in cultivation from season to season. The results of change detection show that cotton exhibited maximum positive change while kharif fodder showed maximum negative change during 2005-2012. Transformation of cotton area to rice cultivation is less conspicuous. The water consumption in upper LCC regions with similar crops is relatively higher as compared to lower regions. Groundwater recharge results revealed that, during the kharif cropping seasons, rainfall is the main source of recharge followed by field percolation losses, while for rabi cropping seasons, canal seepage remains the major source. Seasonal net groundwater recharge is mainly positive during all kharif seasons with a gradual increase in groundwater level in major parts of LCC. Model optimization indicates that PP is more flexible and robust as compared to manual and zone based approaches. Different statistical indicators show that this method yields reliable calibration and validation as values of Nash Sutcliffe Efficiency are 0.976 and 0.969, % BIAS are 0.026 and -0.205 and root mean square errors are 1.23 m and 1.31 m, respectively. Results of model output sensitivity suggest that hydraulic conductivity is a more influential parameter in the study area than drain/fillable porosity. Model simulation results under different scenarios show that rice cultivation has the highest impact on groundwater levels in upper LCC regions whereas major negative changes are observed for lower parts under decreased kharif fodder area in place of rice, cotton and sugarcane. Fluctuations in groundwater level among different proposed LULC scenarios are within ±1 m, thus showing a limited potential for groundwater management. For future climate scenarios, a rise in groundwater level is observed for 2011 to 2025 under H3A2 emission regime. Nevertheless, a drop in groundwater level is expected due to increased crop consumptive water use and decreased precipitations under H3A2 scenario for the periods 2026-2035 and 2036-2045. Although no imminent threat of groundwater shortage is anticipated, there is an opportunity for developing groundwater resources in the lower model regions through water re-allocation that would be helpful in dealing water shortages. The groundwater situation under H3B2 emission regime is relatively complex due to very low expectation of rise in groundwater level through precipitation during 2011-2025. Any positive change in groundwater under such scenarios is mainly associated with changes in crop consumptive water uses. Consequently, water management under such situation requires revisiting of current cropping patterns as well as augmenting water supply through additional surface water resources.:ABSTRACT VIII ZUSAMMENFASSUNG X ACRONYMS 1 Chapter 1 3 GENERAL INTRODUCTION 3 1 Groundwater for irrigated agriculture 3 2 Groundwater development in Pakistan 4 3 Study area 6 4 History of groundwater use in the study area 7 5 Research agenda 8 5.1 Problem statement 8 5.2 Objectives and scope of the study 9 Chapter 2 12 OVERVIEW OF PUBLICATIONS 12 Chapter 3 16 GENERAL CONCLUSIONS AND POLICY RECOMMENDATIONS 16 REFERENCES 20 ANNEXES 23 ACKNOWLEDGEMENTS 123

The work describes the processing and analysis of remote sensing time series data for a comparative assessment of changes in different tropical rainforest areas in East Africa. In order to assess the effects of the derived changes in land cover and forest fragmentation, the study made use of spatially explicit modelling approaches within a geographical information system (GIS) to extrapolate sets of biological field findings in space and time. The analysis and modelling results were visualised aiming to consider the requirements of three different user groups. In order to evaluate measures of forest conservation and to derive recommendations for an effective forest management, quantitative landscape-scale assessments of land cover changes and their influence on forest biodiversity patterns are needed. However, few remote sensing studies have accounted for all of the following aspects at the same time: (i) a dense temporal sequence of land cover change/forest fragmentation information, (ii) the coverage of several decades, (iii) the distinction between multiple forest formations and (iv) direct comparisons of different case studies. In regards to linkages of remote sensing with biological field data, no attempts are known that use time series data for quantitative statements of long-term landscape-scale biodiversity changes. The work studies three officially protected forest areas in Eastern Africa: the Kakamega-Nandi forests in western Kenya (focus area) and Mabira Forest in south-eastern Uganda as well as Budongo Forest in western Uganda (for comparison purposes). Landsat imagery of in total eight or seven dates in regular intervals from 1972/73 to 2003 was used. Making use of supervised multispectral image classification procedures, in total, 12 land cover classes (six forest formations) were distinguished for the Kakamega-Nandi forests and for Budongo Forest while for Mabira Forest ten classes could be realised. An accuracy assessment via error matrices revealed overall classification accuracies between 81% and 85%. The Kakamega-Nandi forests show a continuous decrease between 1972/73 and 2001 of 31%, Mabira Forest experienced an abrupt loss of 24% in the late 1970s/early 1980s, while Budongo Forest shows a relatively stable forest cover extent. An assessment of the spatial patterns of forest losses revealed congruence with areas of high population density while a spatially explicit forest fragmentation index indicates a strong correlation of forest fragmentation with forest management regime and forest accessibility by roads. For the Kenyan focus area, three sets of biological field abundance data on keystone species/groups were used for a quantitative assessment of the influence of long-term changes in tropical forests on landscape-scale biodiversity patterns. For this purpose, the time series was extended with another three land cover data sets derived from aerial photography (1965/67, 1948/(52)) and old topographic maps (1912/13). To predict the spatio-temporal distribution of the army ant Dorylus wilverthi and of ant-following birds, GIS operators (i.e. focal and local functions) and statistical tests (i.e. OLS or SAR regression models) were combined into a spatial modelling procedure. Abundance data on three guilds of birds differing in forest dependency were directly extrapolated to five forest cover classes as distinguished in the time series. The results predict declines in species abundances of 56% for D. wilverthi, of 58% for ant-following birds and an overall loss of 47% for the bird habitat guilds, which in all three cases greatly exceed the rate of forest loss (31%). Additional extrapolations on scenarios of deforestation and reforestation confirmed the negative ecological consequences of splitting-up contiguous forest areas but also showed the potential of mixed indigenous forest plantings. The visualisation of the analysis and modelling results produced a mixture of different outcomes. Map series and a matrix of maps both showing species distributions aim to address scientists and decision makers. The results of the land cover change analysis were synthesised in a map of land cover development types for each study area, respectively. These maps are designed mainly for scientists. Additional maps of change, limited to a single class of forest cover and to three dates were generated to ensure an easy-to-grasp communication of the major forest changes to decision makers. Additionally, an easy-to-handle visualisation tool to be used by scientists, decision makers and local people was developed. For the future, an extension of this study towards a more complete assessment including more species/groups and also ecosystem functions and services would be desirable. Combining a framework for land cover simulation with a framework for running empirical extrapolation models in an automated manner could ideally result in a GIS-based, integrated forest ecosystem assessment tool to be used as regional spatial decision support system. / Die Arbeit beschreibt die Prozessierung und Analyse von Fernerkundungs-Zeitreihendaten für eine vergleichende Abschätzung von Veränderungen verschiedener tropischer Waldökosysteme Ostafrikas. Um Effekte der Veränderungen bzgl. Landbedeckung und Waldfragmentierung auf Biodiversitätsmuster abzuschätzen, wurden verschiedene räumlich explizite Modellierungssätze innerhalb eines geographischen Informationssystems (GIS) zur räumlichen und zeitlichen Extrapolation biologischer Felderhebungsdaten benutzt. Die Visualisierung der Analyse- und Modellierungsergebnisse erfolgte unter Berücksichtigung der Bedürfnisse von drei verschiedenen Nutzergruppen. Um Waldschutzmaßnahmen zu evaluieren und Empfehlungen für ein effektives Waldmanagement abzuleiten, sind quantitative Abschätzungen von Landbedeckungsveränderungen sowie von deren Einfluss auf tropische Waldbiodiversitätsmuster nötig. Wenige fernerkundungsbasierte Studien haben jedoch bislang alle der folgenden Faktoren berücksichtigt: (i) Informationen zu Veränderungen von Landbedeckung und Waldfragmentierung in dichter zeitlicher Sequenz, (ii) die Abdeckung mehrerer Jahrzehnte, (iii) die Unterscheidung zwischen mehreren Waldformationen, und (iv) direkte Vergleiche von unterschiedlichen Fallstudien. Hinsichtlich Verknüpfungen von Fernerkundung mit biologischen Felddaten sind bisher keine Studien bekannt, die Zeitreihendaten für quantitative Aussagen zu Langzeitveränderungen von Biodiversität auf Landschaftsebene verwenden. Die Arbeit untersucht drei offiziell geschützte Gebiete: die Kakamega-Nandi forests in Westkenia (Hauptuntersuchungsgebiet) sowie Mabira Forest in Südost-Uganda und Budongo Forest in West-Uganda (zu Vergleichszwecken). Es wurden Landsat-Daten für insgesamt acht bzw. sieben Zeitpunkte zwischen 1972/73 und 2003 in ungefähr gleichen Abständen erworben. Mit Hilfe von überwachten, multispektralen Klassifizierungsverfahren wurden für die Kakamega-Nandi forests und Budongo Forest jeweils 12 Landbedeckungsklassen (sechs Waldformationen) und für Mabira Forest zehn Klassen unterschieden. Eine Genauigkeitsprüfung mit Hilfe von Fehlermatrizen ergab Gesamtklassifizierungsgenauigkeiten zwischen 81% und 85%. Die Kakamega-Nandi forests sind durch eine kontinuierliche Waldabnahme von 31% zwischen 1972/73 und 2001 gekennzeichnet, Mabira Forest zeigt einen abrupten Waldverlust von 24% in den späten 1970ern/frühen 1980ern, während die Ergebnisse für Budongo Forest eine relativ stabile Waldbedeckung ausweisen. Während eine Abschätzung der räumlichen Muster von Waldverlusten eine hohe Deckungsgleichheit mit Gebieten hoher Bevölkerungsdichte ergab, deutet die Anwendung eines räumlich expliziten Waldfragmentierungsindexes auf eine starke Korrelation von Waldfragmentierung mit der Art von Waldmanagement sowie mit der Erreichbarkeit von Wald über Straßen hin. Um den Einfluss von Langzeit-Landbedeckungsveränderungen auf Biodiversitätsmuster auf Landschaftsebene für das kenianische Hauptuntersuchungsgebiet quantitativ abzuschätzen wurden drei Datensätze mit biologischen Felderhebungen zur Abundanz von Schlüsselarten/-gruppen verwendet. Zu diesem Zweck wurde die Zeitreihe zunächst um drei weitere Landbedeckungs-Datensätze ergänzt, die aus Luftbildern (1965/67, 1948/(52)) bzw. alten topographischen Karten (1912/13) gewonnen wurden. Zur Vorhersage der raum-zeitlichen Verteilung der Treiberameise Dorylus wilverthi wurden GIS-Operatoren und statistische Tests (OLS bzw. SAR Regressionsmodelle) in einem räumlichen Modellierungsablauf kombiniert. Abundanzdaten von drei sich hinsichtlich ihrer Abhängigkeit von Wald unterscheidenden Vogelgilden wurden direkt auf fünf Waldbedeckungsklassen hochgerechnet, die in der Zeitreihe unterschieden werden konnten. Die Ergebnisse prognostizieren Abundanzabnahmen von 56% für D. wilverthi, von 58% für Ameisen-folgende Vögel und einen Gesamtverlust von 47% für die Vogelgilden, was in allen drei Fällen eine deutliche Überschreitung der Waldverlustrate von 31% darstellt. Zusätzliche Extrapolationen basierend auf Szenarien bestätigten die negativen ökologischen Konsequenzen der Zerteilung zusammenhängender Waldflächen bzw. zeigten andererseits das Potential von Aufforstungen mit einheimischen Arten auf. Die Visualisierung der Analyse- bzw. Modellierungsergebnisse führte zu unterschiedlichen Darstellungen: mit einer Reihe von nebeneinander positionierten Einzelkarten sowie einer Matrix von Einzelkarten, die jeweils Artenverteilungen zeigen, sollen Wissenschaftler und Entscheidungsträger angesprochen werden. Aus den Ergebnissen der Landbedeckungsanalyse für die drei Untersuchungsgebiete wurden Landbedeckungsveränderungstypen generiert und jeweils in einer synthetischen Karte dargestellt, die hauptsächlich für Wissenschaftler gedacht sind. Um die wesentlichen Waldveränderungen auch auf einfache Weise zu den Entscheidungsträgern zu kommunizieren, wurden zusätzliche Karten erstellt, die nur eine aggregierte Klasse „Waldbedeckung“ zeigen und jeweils auf drei Zeitschritte der Zeitreihen begrenzt sind. Zusätzlich wurde ein leicht zu bedienendes Visualisierungstool entwickelt, das für Wissenschaftler, Entscheidungsträger und die lokale Bevölkerung gedacht ist. Für die Zukunft wäre eine umfassendere Abschätzung unter Berücksichtigung zusätzlicher Arten/-gruppen sowie auch Ökosystemfunktionen und –dienstleistungen wünschenswert. Die Verknüpfung einer Applikation zur Landbedeckungsmodellierung mit einer Applikation zur Ausführung von empirischen Extrapolationsmodellen (in stärkerem Maße automatisiert als in dieser Arbeit) könnte im Idealfall in ein GIS-basiertes Tool zur integrativen Bewertung von Waldökosystemen münden, das dann als räumliches Entscheidungsunterstützungssystem verwendet werden könnte.

Sudan as a Sahelian country faced numerous drought periods resulting in famine and mass immigration. Spatial data on dynamics of land use and land cover is scarce and/or almost nonexistent. The study area in the North Kordofan State is located in the centre of Sudan and falls in the Sahelian eco-climatic zone. The region generally yields reasonable harvests of rainfed crops and the grasslands supports plenty of livestock. But any attempts to develop medium- to longterm strategies of sustainable land management have been hampered by the impacts of drought and desertification over a long period of time. This study aims to determine and analyse the dynamics of change of land use/land cover classes. The study attempts also to improve classification accuracy by using different data transformation methods like PCA, TCA and CA. In addition it tries to investigate the most reliable methods of pre-classification and/or post-classification change detection. The research also attempts to assess the desertification process using vegetation cover as an indicator. Preliminary mapping of major soil types is also an objective of this study. Landsat data of MSS 187/51 acquired on 01.01.1973 and ETM+ 174/51 acquired on 16.01.2001 were used. Visual interpretation in addition to digital image processing was applied to process the imagery for determining land use/land cover classes for the recent and reference image. Pre- and post-classification change detection methods were used to detect changes in land use/land cover classes in the study area. Pre-classification methods include image differencing, PC and Change Vector Analysis. Georeferenced soil samples were analysed to measure physical and chemical parameters. The measured values of these soil properties were integrated with the results of land use/ land cover classification. The major LULC classes present in the study area are forest, farm on sand, farm on clay, fallow on sand, fallow on clay, woodyland, mixed woodland, grassland, burnt/wetland and natural water bodies. Farming on sandy and clay soils constitute the major land use in the area, while mixed woodland constitutes the major land cover. Classification accuracy is improved by adopting data transformation by PCA, TCA and CA. Pre-classification change detection methods show indistinct and sketchy patterns of change but post-classification method shows obvious and detailed results. Vegetation cover changes were illustrated by use of NDVI. In addition preliminary soil mapping by using mineral indices was done based on ETM+ imagery. Distinct patterns of clay, gardud and sand areas could be classified. Remote sensing methods used in this study prove a high potential to classify land use/land cover as well as soil classes. Moreover the remote sensing methods used confirm efficiency for detecting changes in LULC classes and vegetation cover during the addressed period.

Land use and land cover changes (LULCC) including deforestation for agricultural land and others are elements that contribute on global environmental change. Therefore understanding a trend of these changes in the past, current, and future is important for making proper decisions to develop in a sustainable way. This study analyzed land use and land cover (LULC) changes over time for Tuy Duc district belonging to Dak Nong province based on LULC maps classified from a set of multidate satellite images captured in year 2003, 2006, 2009, and 2013 (SPOT 5 satellite images). The LULC spatio-temporal changes in the area were classified as perennial agriculture, cropland, residential area, grassland, natural forest, plantation and water surface. Based on these changes over time, potential LULC in 2023 was predicted using Cellular Automata (CA)–Markov model. The predicted results of the change in LULC in 2023 reveal that the total area of forest will lose 9,031ha accounting of 50% in total area of the changes. This may be mainly caused by converting forest cover to agriculture (account for 28%), grassland (12%) and residential area (9%). The findings suggest that the forest conversion needs to be controlled and well managed, and a reasonable land use plan should be developed in a harmonization way with forest resources conservation. / Thay đổi sử dụng đất và thảm phủ (LULCC) bao gồm cả việc phá rừng để phát triển nông nghiệp và vì các mục đích khác là tác nhân đóng góp vào biến đổi môi trường toàn cầu. Vì vậy hiểu biết về khuynh hướng của sự thay đổi này trong quá khứ, hiện tại và tương lai là quan trọng để đưa ra những quyết định dúng đắn để phát triển bền vững. Nghiên cứu đã phân tích LULCC trong thời gian qua dựa vào các bản đồ sử dụng đất và thảm phủ (LULC) đã được phân loại từ một loạt ảnh vệ tinh đa phổ được thu chụp vào năm 2003, 2006, 2009 (ảnh SPOT 5). Những thay đổi LULC theo thời gian và không gian trong khu vực được phân loại thành đất nông nghiệp với cây dài ngày, cây ngắn ngày, thổ cư, trảng cỏ cây bụi, rừng tự nhiên, rừng trồng và mặt nước. Dựa trên sự thay đổi này theo thời gian, LULC tiềm năng cho năm 2023 đã được dự báo bằng cách sử dụng mô hình CAMarkov. Kết quả dự báo LULCC năm 2023 đã cho thấy tổng diện tích rừng bị mất khoảng 9,031 ha chiếm 50% trong tổng số diện tích thay đổi. Điều này chủ yếu là do chuyển đổi từ rừng tự nhiên sang canh tác nông nghiệp (chiếm 28%), trảng cỏ cây bụi (12%) và khu dân cư (9%). Kết quả cho thấy việc chuyển đổi rừng cần phải được kiểm soát và quản lý tốt và một kế hoạch sử dụng đất hợp lý cần được xây dựng trong sự hài hòa với bảo tồn tài nguyên rừng.

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The savannas of southern Africa are a highly variable and globally-important biome supporting rapidly-expanding human populations, along with one of the greatest concentrations of wildlife on the continent. Savannas occupy a fifth of the earth's land surface, yet despite their ecological and economic significance, understanding of the complex couplings and feedbacks that drive spatiotemporal patterns of change are lacking. In Chapter 1 of my dissertation, I discuss some of the different theoretical frameworks used to understand complex and dynamic changes in savanna structure and composition. In Chapter 2, I evaluate spatial drivers of water quality declines in the Chobe River using spatiotemporal and geostatistical modeling of time series data collected along a transect spanning a mosaic of protected, urban, and developing urban land use. Chapter 3 explores the complex couplings and feedbacks that drive spatiotemporal patterns of land cover (LC) change across the Chobe District, with a particular focus on climate, fire, herbivory, and anthropogenic disturbance. In Chapter 4, I evaluated the utility of Distance sampling methods to: 1) derive seasonal fecal loading estimates in national park and unprotected land; 2) provide a simple, standardized method to estimate riparian fecal loading for use in distributed hydrological water quality models; 3) answer questions about complex drivers and patterns of water quality variability in a semi-arid southern African river system. Together, these findings have important implications to land use planning and water conservation in southern Africa's dryland savanna ecosystems. / Ph. D.

Human-induced climate change alters global weather patterns, increasing the frequency and severity of droughts. Acting as drivers of land degradation, droughts negatively impact the environment, exacerbating food and water insecurity and threatening livelihoods. However, the complex relationship between drought events and subsequent changes in land cover on a global scale remain insufficiently explored, necessitating further research. This study aims to address this research gap by examining the correlation between global drought events and land cover changes, while also exploring variations between countries' different levels of economic development.  To accomplish this, global precipitation data and the 12-month Standardized Precipitation Index (SPI-12) were employed to identify areas affected by drought, followed by an analysis of changes in land cover within these regions. Utilising the capabilities of Google Earth Engine (GEE) allowed for the evaluation of the relationship between drought events and land cover change.  The findings revealed a correlation between drought events and changes in land cover, with variations observed across different world regions. These results were strengthened by the comparison of land cover changes in non-drought areas. When examining trends regarding types of land cover alterations, it appears that Tree cover, as well as Shrub-, and Grassland, has reduced in regions impacted by drought. Conversely, increases are observed in Cropland and Urban areas. When assessing countries based on economic development, the overall trends were consistent with the global results, although with variations between Advanced Economies and Emerging-, and Developing Economies.  Overall, this study acknowledges drought as a driver of land cover change, while also emphasising the influence of other factors such as anthropogenic activities. These findings offer insights into the complex interactions between droughts and terrestrial ecosystems, and emphasises the importance of strategies for sustainable land management and adaptation efforts, globally. / Människans påverkan på klimatet förändrar globala vädermönster och ökar både frekvensen och omfattningen av torka. Torka orsakar förändringar i markanvändning, påverkar miljön negativt, leder till bristande tillgång på livsmedel och vatten samt hotar möjligheter till försörjning. Det komplexa sambandet mellan torka och markanvändningsförändringar på global nivå är i stort sett outforskat och kräver därför ytterligare forskning. Denna studie syftar till att fylla detta kunskapsgap genom att undersöka sambandet mellan globala perioder av torka och markanvändningsförändringar, samt hur faktorer såsom ekonomisk utveckling påverkar markförändringar.  Detta uppnåddes genom att använda global nederbördsdata och 12-månaders ‘Standardized Precipitation Index’ (SPI-12). Områden som har påverkats av torka identifierades, varpå en analys av markanvändningsförändringar inom dessa områden utfördes. Analysen gjordes med hjälp av det geospatiala analysverktyget Google Earth Engine (GEE).  Resultatet visade på en korrelation mellan torka och markanvändningsförändringar, samtidigt som regionala variationer observerades. Detta samband förstärktes genom att undersöka korrelationen mellan markanvändningsförändringar inom områden ej utsatta för torka. Gällande trender i typer av markanvändningsförändringar verkar trädbevuxna områden samt busk- och gräsmark ha minskat i regioner påverkade av torka. Åkermark och urbana områden verkar däremot ha ökat. Vid bedömning av länder baserat på ekonomisk utveckling var de övergripande trenderna i linje med de globala resultaten, även om variationer noterades mellan avancerade ekonomier och tillväxt- och utvecklingsekonomier.  Sammanfattningsvis identifierar denna studie torka som en drivkraft för förändringar i markanvändning, samtidigt som den betonar betydelsen av mänskliga aktiviteter. Dessa resultat ger insikt i de komplexa interaktionerna mellan torka och terrestra ekosystem, samt understryker vikten av strategier för hållbar markförvaltning och anpassning insatser på global nivå.