Atualmente, uma atividade que se tornou estratégica a nível nacional é o cultivo de espécies oleaginosas para o mercado alimentício e energético, em especial o plantio da palma de óleo (dendê) na região nordeste do estado do Pará, na Amazônia oriental. Esta cultura, assim como tem apresentado benefícios, como fixação do homem no campo, recuperação de áreas degradadas e redução da perda de solo, também tem apresentado riscos de ordem social e ambiental, como possíveis expropriações de terras e aumento do desmatamento e empobrecimento da diversidade ecológica. Mas se conhece pouco ainda dos impactos da expansão dessa cultura sobre o balanço hídrico e processos erosivos. Por isso, este trabalho estimou os impactos da expansão da cultura da palma de óleo na dinâmica de mudança de uso e cobertura da terra, bem como no escoamento superficial e na produção de sedimentos. Para isto, aplicou-se o modelo hidrossedimentológico Soil and Water Assessment Tool (SWAT) e o modelo de dinâmica espacial Conversion of Land Use and its Effects at Small region extent (CLUES) sobre as sub-bacias não monitoradas dos rios Bujaru (SBRB) e Mariquita (SBRM), com calibração do SWAT realizada a partir da técnica de regionalização de vazão por regressão não linear e medições em campo com o molinete hidrométrico. A princípio, as equações de regressão se apresentaram eficientes nas estimativas de dados de vazão para as sub-bacias, fundamentadas no bom resultado da calibração e validação sobre as estações reais. Nas áreas de palma de óleo, o modelo foi capaz de estimar com bom grau de eficiência a evapotranspiração nestas Unidades de Resposta Hidrológica da SBRB (1089,2 mm) e da SBRM (1093,1 mm), em relação a literatura e medidas em torre de monitoramento micrometeorológico. O modelo CLUE-S foi capaz de integralizar as variáveis explanatórias com as demandas agregadas e as características de elasticidade com o objetivo de gerar cenários futuros de uso e cobertura da terra, bem como modelar a palma de óleo nas sub-bacias, identificando as variáveis biofísicas como as principais forçantes de mudança de uso e cobertura da terra. As estimativas de escoamento superficial e produção de sedimentos apontaram para uma redução na SBRB e um aumento na SBRM entre os cenários de 2008, 2013 e o cenário projetado com o CLUE-S para 2023 em especial por razão da grande variação das áreas de vegetação secundária. As áreas de Palma de Óleo tiveram menor escoamento superficial e produção de sedimentos médio mensal do período mais chuvoso em ambas sub-bacias e em todos os cenários em relação as áreas de Agricultura Geral e Pastagem. Os resultados também mostraram a boa capacidade do uso integrado dos modelos SWAT e CLUE-S na geração de dados que contribuem para a análise do impacto ambiental da expansão da palma de óleo na região nordeste do estado do Pará, sendo também importante para o planejamento e gestão ambiental rural em bacias hidrográficas não monitoradas na Amazônia Oriental, pois demonstra a eficiência do método em proporcionar o aumento dos conhecimentos do comportamento hidrológico destas bacias em relação a dinâmica espacial de uso e cobertura do solo. / Nowadays, oilseed production for food and energy has become a strategic activity at national level in Brazil, particularly oil palm crops located in the Northeast of Pará State, Eastern Amazon. Oil palm crops have shown benefits such as keeping farmers on the land, recovering degraded areas and reducing soil loss. Conversely, it may also increase social and environmental risks linked to land tenure instability and land expropriation, deforestation and biodiversity losses. In such context, there is still a lack of knowledge concerning the impacts of such crop on the local water balance and erosion processes. Thus, this research estimated the impacts of increasing oil palm crops on land use and land cover change dynamics, as well as on runoff and soil erosion processes. To do so, it was applied the Soil and Water Assessment Tool (SWAT) and the spatial explicit framework Conversion of Land Use and its Effects at Small region extent (CLUE-S) over two ungauged sub-basins of Bujaru (SBRB) and Mariquita (SBRM) rivers. SWAT calibration was done by the regionalization streamflow method that adopts nonlinear regression and field measurements using a current meter. Initially, regression equations were effective in streamflow data estimation for the subbasins, this was based on effective calibration and validation results upon real stations. In the oil palm crop areas, the SWAT modeling was able to successfully estimate evapotranspiration on both hydrologic response unit of SBRB (1089,2 mm) and SBRM (1093,1mm) when compared to the literature and measures in micrometeorological monitoring tower. When applying CLUE-S model it was capable to integrate explanatory variables to scenario demands and elasticity parameters determining land use/cover change. Such integration allowed modelling oil palm spatial-temporal dynamics in current and future scenario demands within the two sub-basins SBRB and SBRM, as well as the identification of biophysical variables as the core drivers of land use/cover change. Runoff and sediment yield pointed out towards a decline in SBRB and an increase in SBRM in the current scenario between 2008 and 2013, as well as in the future scenario modelled using CLUE-S land use/cover change maps for 2023, particularly because of a large variation in the dynamics of secondary vegetation between the two sub-basins. Oil palm areas had smaller monthly average runoff and sediment yield in the rainiest period in both sub-basins and in current and future scenarios regarding agriculture and pasture areas. The results also show a suitable capability of integration between SWAT and CLUE-S models when generating data that contribute to the analysis of environmental impact of oil palm expansion in the Northeast of Para State. Such contribution is also relevant to the rural environmental planning and management in ungauged river-basins in Eastern Amazon, since the results found here demonstrate the efficiency of the method in providing an improved knowledge of the hydrological behavior of these basins concerning land use and land cover changes dynamics.

Unidades de conservação possuem o objetivo de preservar a natureza, evitando o desmatamento e promovendo a sustentabilidade do meio ambiente. Contudo, para que estas atendam aos propósitos para os quais foram criadas, sem acarretar prejuízos sociais ou conflitos com as populações locais, estudos aplicados interdisciplinares são essenciais, agregando conhecimento útil à gestão e ao planejamento das unidades de conservação. Sob a ótica da ciência da complexidade, o objetivo principal deste trabalho foi desenvolver modelos que auxiliassem na compreensão das mudanças no uso e cobertura da terra, realizassem simulações de cenários futuros, e permitissem observar os efeitos da implantação de políticas de preservação sobre a paisagem. Construímos modelos dinâmicos baseados em cadeias de Markov e autômatos celulares, aliados a técnicas de geoprocessamento. Os modelos foram aplicados a um estudo de caso, o Parque estadual do Itinguçu, ao longo de uma série temporal de materiais aerofotográficos de quase 50 anos (1962-2010). Os resultados dos modelos mostraram que a implantação da unidade de conservação foi essencial para barrar o desmatamento, mas que as práticas tradicionais de agricultura itinerante não estavam diretamente relacionadas à conversão da área de floresta, indicando que a incompatibilidade entre preservação e presença humana, muitas vezes usada como justificativa para a implantação de unidades de proteção integral, deve ser reavaliada sob outra perspectiva. Os resultados também apontaram para um desempenho satisfatório do modelo de Markov em projetar tendências, apesar de possuir certa aleatoriedade na alocação dos elementos no espaço. O incremento do autômato celular diminuiu tal aleatoriedade, mas não foi tão eficiente em reproduzir as tendências observadas nas matrizes de transição quanto o modelo de Markov. Concluímos que a metodologia aplicada no presente trabalho foi útil para compreendermos as mudanças na paisagem da área de estudo, e que a escolha do modelo (Markov ou Markov com autômato celular) deve ser feita com base em uma análise criteriosa caso a caso, em conformidade com as prioridades do estudo a ser realizado. Espera-se que esta pesquisa possa fomentar a discussão sobre o uso desta metodologia como uma ferramenta para planejamento e análise de políticas de conservação da natureza e gestão do território / Conservation units have the purpose to preserve the nature, avoiding the deforestation and promoting the environment sustainability. However, for these to be effective in that purpose, without causing social injuries or conflicts with the local population, interdisciplinary applied studies are essential and must be made by different areas of science, adding useful knowledge to the management of protected areas. Under the vision of the Complexity Science, the main goal of this research was to develop models that help in understanding the land use and cover changes, perform simulations of future scenarios, and allow observing the effects of the implementation of conservation policies on the landscape. We built Markov and cellular automata models, allied to the geoprocessing techniques. The models were applied to a case study, the Parque Estadual do Itinguçu, over a time series of aero photographic materials of almost 50 years (1962-2010). The results of the models showed that the implementation of the conservation unit was essential to stop the deforestation, but the traditional practices of shifting cultivation were not directly related to the conversion of forest area, indicating that the incompatibility between conservation and human presence, often used as justification for the implementation of some strict protection units, should be reviewed from a different perspective. The results also pointed to a satisfactory performance of the Markov model to project trends, despite having certain randomness in the allocation of elements in space. Add cellular automata to model decreased this randomness, but was not so effective in reproducing the observed trends in transition matrices than the Markov model. We concluded that the methodology applied in this study was useful for understanding the changes in the landscape of the study area, and that the choice of model (Markov or Markov with cellular automata) should be based on a careful analysis in accordance with the priorities of the study to be applied. We hope that this research can encourage the discussion of this methodology as a tool for analysis of conservation policies of nature and land management

Unidades de conservação possuem o objetivo de preservar a natureza, evitando o desmatamento e promovendo a sustentabilidade do meio ambiente. Contudo, para que estas atendam aos propósitos para os quais foram criadas, sem acarretar prejuízos sociais ou conflitos com as populações locais, estudos aplicados interdisciplinares são essenciais, agregando conhecimento útil à gestão e ao planejamento das unidades de conservação. Sob a ótica da ciência da complexidade, o objetivo principal deste trabalho foi desenvolver modelos que auxiliassem na compreensão das mudanças no uso e cobertura da terra, realizassem simulações de cenários futuros, e permitissem observar os efeitos da implantação de políticas de preservação sobre a paisagem. Construímos modelos dinâmicos baseados em cadeias de Markov e autômatos celulares, aliados a técnicas de geoprocessamento. Os modelos foram aplicados a um estudo de caso, o Parque estadual do Itinguçu, ao longo de uma série temporal de materiais aerofotográficos de quase 50 anos (1962-2010). Os resultados dos modelos mostraram que a implantação da unidade de conservação foi essencial para barrar o desmatamento, mas que as práticas tradicionais de agricultura itinerante não estavam diretamente relacionadas à conversão da área de floresta, indicando que a incompatibilidade entre preservação e presença humana, muitas vezes usada como justificativa para a implantação de unidades de proteção integral, deve ser reavaliada sob outra perspectiva. Os resultados também apontaram para um desempenho satisfatório do modelo de Markov em projetar tendências, apesar de possuir certa aleatoriedade na alocação dos elementos no espaço. O incremento do autômato celular diminuiu tal aleatoriedade, mas não foi tão eficiente em reproduzir as tendências observadas nas matrizes de transição quanto o modelo de Markov. Concluímos que a metodologia aplicada no presente trabalho foi útil para compreendermos as mudanças na paisagem da área de estudo, e que a escolha do modelo (Markov ou Markov com autômato celular) deve ser feita com base em uma análise criteriosa caso a caso, em conformidade com as prioridades do estudo a ser realizado. Espera-se que esta pesquisa possa fomentar a discussão sobre o uso desta metodologia como uma ferramenta para planejamento e análise de políticas de conservação da natureza e gestão do território / Conservation units have the purpose to preserve the nature, avoiding the deforestation and promoting the environment sustainability. However, for these to be effective in that purpose, without causing social injuries or conflicts with the local population, interdisciplinary applied studies are essential and must be made by different areas of science, adding useful knowledge to the management of protected areas. Under the vision of the Complexity Science, the main goal of this research was to develop models that help in understanding the land use and cover changes, perform simulations of future scenarios, and allow observing the effects of the implementation of conservation policies on the landscape. We built Markov and cellular automata models, allied to the geoprocessing techniques. The models were applied to a case study, the Parque Estadual do Itinguçu, over a time series of aero photographic materials of almost 50 years (1962-2010). The results of the models showed that the implementation of the conservation unit was essential to stop the deforestation, but the traditional practices of shifting cultivation were not directly related to the conversion of forest area, indicating that the incompatibility between conservation and human presence, often used as justification for the implementation of some strict protection units, should be reviewed from a different perspective. The results also pointed to a satisfactory performance of the Markov model to project trends, despite having certain randomness in the allocation of elements in space. Add cellular automata to model decreased this randomness, but was not so effective in reproducing the observed trends in transition matrices than the Markov model. We concluded that the methodology applied in this study was useful for understanding the changes in the landscape of the study area, and that the choice of model (Markov or Markov with cellular automata) should be based on a careful analysis in accordance with the priorities of the study to be applied. We hope that this research can encourage the discussion of this methodology as a tool for analysis of conservation policies of nature and land management

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.

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / O atual nível das mudanças uso do solo causa impactos nas mudanças ambientais globais. Os processos de mudanças do uso e cobertura do solo são processos complexos e não acontecem ao acaso sobre uma região. Geralmente estas mudanças são determinadas localmente, regionalmente ou globalmente por fatores geográficos, ambientais, sociais, econômicos e políticos interagindo em diversas escalas temporais e espaciais. Parte desta complexidade é capturada por modelos de simulação de mudanças do uso e cobertura do solo. Uma etapa do processo de simulação do modelo CLUE-S é a quantificação da influência local dos impulsores de mudança sobre a probabilidade de ocorrência de uma classe de uso do solo. Esta influência local é obtida ajustando um modelo de regressão logística. Um modelo de regressão espacial é proposto como alternativa para selecionar os impulsores de mudanças. Este modelo incorpora a informação da vizinhança espacial existente nos dados que não é considerada na regressão logística. Baseado em um cenário de tendência linear para a demanda agregada do uso do solo, simulações da mudança do uso do solo para a microbacia do Coxim, Mato Grosso do Sul, foram geradas, comparadas e analisadas usando o modelo CLUE-S sob os enfoques da regressão logística e espacial para o período de 2001 a 2011. Ambos os enfoques apresentaram simulações com muito boa concordância, medidas de acurácia global e Kappa altos, com o uso do solo para o ano de referência de 2004. A diferença entre os enfoques foi observada na distribuição espacial da simulação do uso do solo para o ano 2011, sendo o enfoque da regressão espacial que teve a simulação com menor discrepância com a demanda do uso do solo para esse ano. / Present state of land use changes impacts global environmental changes. Land use and cover changes are complex processes and do not occur at random pattern in an area. In general, they are determined locally, regionally and globally by geographic, environmental, social, economic and political factors interacting at diverse temporal and spatial scales. Part of this complexity can be modeled by land use and cover change simulation models. An important step of simulation process in CLUE-S model is local influence of driving forces over the occurrence of a land use type. This influence is obtained by logistic regression model. A spatial lag regression model is proposed to select driving forces. This model incorporates spatial neighborhood information which is ignored by logistic regression. Based on a lineal trend scenario of land use demand, simulations of land use changes for Coxim microbasin, Mato Grosso do Sul, were generated, analyzed and compared using CLUE-S model under logistic and spatial regression approaches. The period of simulations was 2001-2011. Both approaches revealed elevated concordance, high global accuracy and Kappa index, to land use for 2004 reference year. Differences were observed for spatial distribution for land use simulations for 2011. Spatial lag regression simulation for 2011 reached less discrepancy to land use demand for that year.

Atualmente, uma atividade que se tornou estratégica a nível nacional é o cultivo de espécies oleaginosas para o mercado alimentício e energético, em especial o plantio da palma de óleo (dendê) na região nordeste do estado do Pará, na Amazônia oriental. Esta cultura, assim como tem apresentado benefícios, como fixação do homem no campo, recuperação de áreas degradadas e redução da perda de solo, também tem apresentado riscos de ordem social e ambiental, como possíveis expropriações de terras e aumento do desmatamento e empobrecimento da diversidade ecológica. Mas se conhece pouco ainda dos impactos da expansão dessa cultura sobre o balanço hídrico e processos erosivos. Por isso, este trabalho estimou os impactos da expansão da cultura da palma de óleo na dinâmica de mudança de uso e cobertura da terra, bem como no escoamento superficial e na produção de sedimentos. Para isto, aplicou-se o modelo hidrossedimentológico Soil and Water Assessment Tool (SWAT) e o modelo de dinâmica espacial Conversion of Land Use and its Effects at Small region extent (CLUES) sobre as sub-bacias não monitoradas dos rios Bujaru (SBRB) e Mariquita (SBRM), com calibração do SWAT realizada a partir da técnica de regionalização de vazão por regressão não linear e medições em campo com o molinete hidrométrico. A princípio, as equações de regressão se apresentaram eficientes nas estimativas de dados de vazão para as sub-bacias, fundamentadas no bom resultado da calibração e validação sobre as estações reais. Nas áreas de palma de óleo, o modelo foi capaz de estimar com bom grau de eficiência a evapotranspiração nestas Unidades de Resposta Hidrológica da SBRB (1089,2 mm) e da SBRM (1093,1 mm), em relação a literatura e medidas em torre de monitoramento micrometeorológico. O modelo CLUE-S foi capaz de integralizar as variáveis explanatórias com as demandas agregadas e as características de elasticidade com o objetivo de gerar cenários futuros de uso e cobertura da terra, bem como modelar a palma de óleo nas sub-bacias, identificando as variáveis biofísicas como as principais forçantes de mudança de uso e cobertura da terra. As estimativas de escoamento superficial e produção de sedimentos apontaram para uma redução na SBRB e um aumento na SBRM entre os cenários de 2008, 2013 e o cenário projetado com o CLUE-S para 2023 em especial por razão da grande variação das áreas de vegetação secundária. As áreas de Palma de Óleo tiveram menor escoamento superficial e produção de sedimentos médio mensal do período mais chuvoso em ambas sub-bacias e em todos os cenários em relação as áreas de Agricultura Geral e Pastagem. Os resultados também mostraram a boa capacidade do uso integrado dos modelos SWAT e CLUE-S na geração de dados que contribuem para a análise do impacto ambiental da expansão da palma de óleo na região nordeste do estado do Pará, sendo também importante para o planejamento e gestão ambiental rural em bacias hidrográficas não monitoradas na Amazônia Oriental, pois demonstra a eficiência do método em proporcionar o aumento dos conhecimentos do comportamento hidrológico destas bacias em relação a dinâmica espacial de uso e cobertura do solo. / Nowadays, oilseed production for food and energy has become a strategic activity at national level in Brazil, particularly oil palm crops located in the Northeast of Pará State, Eastern Amazon. Oil palm crops have shown benefits such as keeping farmers on the land, recovering degraded areas and reducing soil loss. Conversely, it may also increase social and environmental risks linked to land tenure instability and land expropriation, deforestation and biodiversity losses. In such context, there is still a lack of knowledge concerning the impacts of such crop on the local water balance and erosion processes. Thus, this research estimated the impacts of increasing oil palm crops on land use and land cover change dynamics, as well as on runoff and soil erosion processes. To do so, it was applied the Soil and Water Assessment Tool (SWAT) and the spatial explicit framework Conversion of Land Use and its Effects at Small region extent (CLUE-S) over two ungauged sub-basins of Bujaru (SBRB) and Mariquita (SBRM) rivers. SWAT calibration was done by the regionalization streamflow method that adopts nonlinear regression and field measurements using a current meter. Initially, regression equations were effective in streamflow data estimation for the subbasins, this was based on effective calibration and validation results upon real stations. In the oil palm crop areas, the SWAT modeling was able to successfully estimate evapotranspiration on both hydrologic response unit of SBRB (1089,2 mm) and SBRM (1093,1mm) when compared to the literature and measures in micrometeorological monitoring tower. When applying CLUE-S model it was capable to integrate explanatory variables to scenario demands and elasticity parameters determining land use/cover change. Such integration allowed modelling oil palm spatial-temporal dynamics in current and future scenario demands within the two sub-basins SBRB and SBRM, as well as the identification of biophysical variables as the core drivers of land use/cover change. Runoff and sediment yield pointed out towards a decline in SBRB and an increase in SBRM in the current scenario between 2008 and 2013, as well as in the future scenario modelled using CLUE-S land use/cover change maps for 2023, particularly because of a large variation in the dynamics of secondary vegetation between the two sub-basins. Oil palm areas had smaller monthly average runoff and sediment yield in the rainiest period in both sub-basins and in current and future scenarios regarding agriculture and pasture areas. The results also show a suitable capability of integration between SWAT and CLUE-S models when generating data that contribute to the analysis of environmental impact of oil palm expansion in the Northeast of Para State. Such contribution is also relevant to the rural environmental planning and management in ungauged river-basins in Eastern Amazon, since the results found here demonstrate the efficiency of the method in providing an improved knowledge of the hydrological behavior of these basins concerning land use and land cover changes dynamics.

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / O atual nível das mudanças uso do solo causa impactos nas mudanças ambientais globais. Os processos de mudanças do uso e cobertura do solo são processos complexos e não acontecem ao acaso sobre uma região. Geralmente estas mudanças são determinadas localmente, regionalmente ou globalmente por fatores geográficos, ambientais, sociais, econômicos e políticos interagindo em diversas escalas temporais e espaciais. Parte desta complexidade é capturada por modelos de simulação de mudanças do uso e cobertura do solo. Uma etapa do processo de simulação do modelo CLUE-S é a quantificação da influência local dos impulsores de mudança sobre a probabilidade de ocorrência de uma classe de uso do solo. Esta influência local é obtida ajustando um modelo de regressão logística. Um modelo de regressão espacial é proposto como alternativa para selecionar os impulsores de mudanças. Este modelo incorpora a informação da vizinhança espacial existente nos dados que não é considerada na regressão logística. Baseado em um cenário de tendência linear para a demanda agregada do uso do solo, simulações da mudança do uso do solo para a microbacia do Coxim, Mato Grosso do Sul, foram geradas, comparadas e analisadas usando o modelo CLUE-S sob os enfoques da regressão logística e espacial para o período de 2001 a 2011. Ambos os enfoques apresentaram simulações com muito boa concordância, medidas de acurácia global e Kappa altos, com o uso do solo para o ano de referência de 2004. A diferença entre os enfoques foi observada na distribuição espacial da simulação do uso do solo para o ano 2011, sendo o enfoque da regressão espacial que teve a simulação com menor discrepância com a demanda do uso do solo para esse ano. / Present state of land use changes impacts global environmental changes. Land use and cover changes are complex processes and do not occur at random pattern in an area. In general, they are determined locally, regionally and globally by geographic, environmental, social, economic and political factors interacting at diverse temporal and spatial scales. Part of this complexity can be modeled by land use and cover change simulation models. An important step of simulation process in CLUE-S model is local influence of driving forces over the occurrence of a land use type. This influence is obtained by logistic regression model. A spatial lag regression model is proposed to select driving forces. This model incorporates spatial neighborhood information which is ignored by logistic regression. Based on a lineal trend scenario of land use demand, simulations of land use changes for Coxim microbasin, Mato Grosso do Sul, were generated, analyzed and compared using CLUE-S model under logistic and spatial regression approaches. The period of simulations was 2001-2011. Both approaches revealed elevated concordance, high global accuracy and Kappa index, to land use for 2004 reference year. Differences were observed for spatial distribution for land use simulations for 2011. Spatial lag regression simulation for 2011 reached less discrepancy to land use demand for that year.

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

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.