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Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type.

New satellite missions have provided High Resolution (HR) Satellite Image Time Series (SITS), offering detailed spatial, spectral, and temporal information for effective monitoring of diverse Earth features including weather, landforms, oceans, vegetation, and agricultural practices. SITS can be used for an accurate understanding of the Land Cover (LC) behavior and providing the possibility of precise mapping of LCs. Moreover, HR SITS presents an unprecedented possibility for the creation and modification of HR Land Cover Change (LCC) and Land Cover Transition (LCT) maps. For the long-term scale, spanning multiple years, it becomes feasible to analyze LCC and the LCTs occurring between consecutive years. Existing methods in literature often analyze bi-temporal images and miss the valuable multi-temporal/multi-annual information of SITS that is crucial for an accurate SITS analysis. As a result, HR SITS necessitates a paradigm shift in processing and methodology development, introducing new challenges in data handling. Yet, the creation of techniques that can effectively manage the high spatial correlation and complementary temporal resolutions of pixels remains paramount. Moreover, the temporal availability of HR data across historical and current archives varies significantly, creating the need for an effective preprocessing to account for factors like atmospheric and radiometric conditions that can affect image reflectance and their applicability in SITS analysis. Flexible and automatic SITS analysis methods can be developed by paying special attention to handling big amounts of data and modeling the correlation and characterization of SITS in space and time. Novel methods should deal with data preparation and pre-processing at large-scale from end-to-end by introducing a set of steps that guarantee reliable SITS analysis while upholding the computational efficiency for a feasible SITS analysis. In this context, the recent strides in deep learning-based frameworks have demonstrated their potential across various image processing tasks, and thus the high relevance for addressing SITS analysis. Deep learning-based methods can be supervised or unsupervised considering their learning process. Supervised deep learning methods rely on labeled training data, which can be impractical for large-scale multi-temporal datasets, due to the challenges of manual labeling. In contrast, unsupervised deep learning methods are favored as they can automatically discover temporal patterns and changes without the need for labeled samples, thereby reducing the computational load, making them more suitable for handling extensive SITS. In this scenario, the objectives of this thesis are mainly three. Firstly, it seeks to establish a robust and reliable framework for the precise mapping of LCs by designing novel techniques for time series analysis. Secondly, it aims to utilize the capacities of unsupervised deep learning methods, such as pretrained Convolutional Neural Networks (CNNs), to construct a comprehensive methodology for Change Detection (CD), thereby mitigating complexity and reducing computational requirements in comparison with supervised methods. This involves the efficient extraction of spatial, spectral, and temporal features from complex multi-temporal, multi-spectral SITS. Lastly, the thesis endeavors to develop novel methods for analyzing LCCs occurring over extended time periods, spanning multiple years. This multifaceted approach encompasses the detection of changes, timing identification, and classification of the specific types of LCTs. The efficacy of the innovative methodologies and associated techniques is showcased through a series of experiments conducted on HR SITS datasets, including those from Sentinel-2 and Landsat. These experiments reveal significant enhancements when compared to existing methods that represent the current state-of-the-art.

The island of La Gonave, ~50 km northwest of Port-au-Prince, represents the subsistence Haitian lifestyle. Little is known about human--environment interactions on La Gonave. The first objective of this research was to investigate landscape dynamics through image classification, change detection, and landscape pattern analysis using Landsat 5 (TM) imagery from 1990--2010. Five land cover classes were considered: Agriculture, Forest/Dense Vegetation (DV), Shrub, Barren/Eroded, and Nonforested Wetlands. Overall image classification accuracy was 87%. The area percent change was -39.7, -22.7, 87.4, -7.0, 10.2%, for the respective classes. Landscape pattern analysis illustrated the encroachment of Shrub in core Forest/DV patches and the decline of Agricultural patch integrity. Agricultural abandonment, deforestation, and forest regrowth generated an increasingly fragmented landscape. The second objective of this research was to better understand the survival of the red-tailed hawk (RTH) on La Gonave by exploring the human--RTH relationship. We implemented a survey (n = 121) in 10 rural villages on La Gonave regarding their perceptions and interactions with the RTH during May--June, 2012. During fieldwork we sighted seven RTHs and found one nest. Many respondents noted the aggressive behavior of RTHs during nesting, suggesting reproductive behavior on the island. Our results indicate that RTHs inhabiting this island were not persecuted, despite intense predation of domestic chickens. Aside from predation near homes, villagers do not interact with the hawk as it remains out of sight. The RTH currently has no known predators, but it remains in danger of island extirpation due to ecological degradation. / Master of Science

<p>The purpose of this thesis is to analyse interactions between freshwater flows, terrestrial ecosystems and human well-being. Freshwater management and policy has mainly focused on the liquid water part (surface and ground water run off) of the hydrological cycle including aquatic ecosystems. Although of great significance, this thesis shows that such a focus will not be sufficient for coping with freshwater related social-ecological vulnerability. The thesis illustrates that the terrestrial component of the hydrological cycle, reflected in vapour flows (or evapotranspiration), serves multiple functions in the human life-support system. A broader understanding of the interactions between terrestrial systems and freshwater flows is particularly important in light of present widespread land cover change in terrestrial ecosystems. </p><p>The water vapour flows from continental ecosystems were quantified at a global scale in Paper I of the thesis. It was estimated that in order to sustain the majority of global terrestrial ecosystem services on which humanity depends, an annual water vapour flow of 63 000 km3/yr is needed, including 6800 km3/yr for crop production. In comparison, the annual human withdrawal of liquid water amounts to roughly 4000 km3/yr. A potential conflict between freshwater for future food production and for terrestrial ecosystem services was identified. </p><p>Human redistribution of water vapour flows as a consequence of long-term land cover change was addressed at both continental (Australia) (Paper II) and global scales (Paper III). It was estimated that the annual vapour flow had decreased by 10% in Australia during the last 200 years. This is due to a decrease in woody vegetation for agricultural production. The reduction in vapour flows has caused severe problems with salinity of soils and rivers. The human-induced alteration of vapour flows was estimated at more than 15 times the volume of human-induced change in liquid water (Paper II). </p>

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.

Orientador: Thiago Sanna Freire Silva / Resumo: As paisagens são a expressão da interação dinâmica entre processos naturais e atividades humanas. A região do Litoral Norte do Estado de São Paulo apresenta uma grande diversidade de fitofisionomias do bioma Mata Atlântica, e um rico patrimônio material e imaterial, em função da histórica interação do homem com a natureza. São inúmeros os vetores que agem simultaneamente sobre essa paisagem, resultando em efeitos cumulativos que transformam sua multifuncionalidade e multidimensionalidade. Este estudo objetivou a compreensão dos vetores de mudanças na paisagem do Litoral Norte do Estado de São Paulo. A transformação histórica da paisagem na área de estudo foi investigada buscando compreender a modificações dos cenários paisagísticos na sua integridade desde o início da colonização europeia na região de estudo. Através desta revisão histórica, demonstramos que a política econômica impulsionou investimentos em instalações tecnológicas e de acesso a região, que influenciaram no aumento das taxas de crescimento populacional, resultando num rápido crescimento das áreas urbanas após meados do século XX. O segundo capítulo quantificou as mudanças físicas da paisagem, através de séries históricas de imagens da série de satélites Landsat, utilizando o algoritmo Random Forests para classificação supervisionada do uso e cobertura da terra. Pudemos então, demonstrar que a região apresenta tendência a uma dicotomia no uso da terra, entre o uso urbano e a conservação ambiental. Entre 1985 e... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Landscapes are an expression of the dynamic interaction between natural environments and human activities. The Northern Coast of São Paulo State has a high diversity of Atlantic Forest vegetation types, and the Serra do Mar mountain range has a rich material and immaterial heritage due to ancient human-nature interactions. There are several different driving forces of change acting together over these landscapes, resulting in a cumulative effect over time. Our study sought to understand the causes and consequences of landscape change in the Northern Coast of São Paulo state, from 1985 to present. We described the land use history and landscape changes since the Europeans arrived in the region, during the XVI century. We identified that national economic policies and interests have led to investment in improved access and technological development, which in turn influenced migration to the region and resulted in fast urban expansion. In the second chapter, we have shown that land use change in the Northern Coast of São Paulo poses a dichotomy between two main land cover change trajectories over 30 years: forest persistence and fast urban growth. We found only 100 km² (8%) of forest disturbance within the State Parks, while dense urban settlements grew 167% outside the park, replacing mainly rural land uses. To identify and understand the driving forces of change in the region, we used Partial Least Squares - Path Modelling to model the relation between driving forces and lands... (Complete abstract click electronic access below) / Doutor

The purpose of this thesis is to analyse interactions between freshwater flows, terrestrial ecosystems and human well-being. Freshwater management and policy has mainly focused on the liquid water part (surface and ground water run off) of the hydrological cycle including aquatic ecosystems. Although of great significance, this thesis shows that such a focus will not be sufficient for coping with freshwater related social-ecological vulnerability. The thesis illustrates that the terrestrial component of the hydrological cycle, reflected in vapour flows (or evapotranspiration), serves multiple functions in the human life-support system. A broader understanding of the interactions between terrestrial systems and freshwater flows is particularly important in light of present widespread land cover change in terrestrial ecosystems. The water vapour flows from continental ecosystems were quantified at a global scale in Paper I of the thesis. It was estimated that in order to sustain the majority of global terrestrial ecosystem services on which humanity depends, an annual water vapour flow of 63 000 km3/yr is needed, including 6800 km3/yr for crop production. In comparison, the annual human withdrawal of liquid water amounts to roughly 4000 km3/yr. A potential conflict between freshwater for future food production and for terrestrial ecosystem services was identified. Human redistribution of water vapour flows as a consequence of long-term land cover change was addressed at both continental (Australia) (Paper II) and global scales (Paper III). It was estimated that the annual vapour flow had decreased by 10% in Australia during the last 200 years. This is due to a decrease in woody vegetation for agricultural production. The reduction in vapour flows has caused severe problems with salinity of soils and rivers. The human-induced alteration of vapour flows was estimated at more than 15 times the volume of human-induced change in liquid water (Paper II).

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.

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.