Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area.

Beaver Creek is an impaired streams that is not supporting its designated use for recreation due to Escherichia coli (E.coli), and sediment. To address this problem, this thesis was divided into two studies. The first study explored changes in Land Use Land Cover (LULC), and its impact on surface water quality. Changes in E.coli load between 1997-2001 and 2014-2018 were analyzed. Also, Landsat data of 2001, and 2018 were examined in Terrset 18.31. Mann-Whitney test only showed a significant reduction in E.coli for one site. Negative correlation was established between E.coli load, and Developed LULC, Forest LULC, and Cultivated LULC. The second study modelled discharge for Beaver Creek watershed using HEC-HMS. This study simulated discharge in an upstream sub-watershed of Beaver Creek, and the full Beaver Creek with a Nash-Sutcliffe of 0.007, and R2 0.20. Sub-basins with high discharge were identified for further examination for possible high sediment load.

No description available.

The unprecedented growth of urban areas all over the globe is nowadays maybe most apparent in China having undergone rapid urbanization since the late 1970s. The need for new residential, commercial and industrial areas leads to new urban regions challenging sustainable development and the maintenance and creation of a high living standard as well as the preservation of ecological functionality. Therefore, timely and reliable information on land-cover changes and their consequent environmental impacts are needed to support sustainable urban development.The objective of this research is the analysis of land-cover changes, especially the development of urban areas in terms of speed, magnitude and resulting implications for the natural and rural environment using satellite imagery and the quantification of environmental impacts with the concepts of ecosystem services and landscape metrics. The study areas are the cities of Shanghai and Stockholm and the three highly-urbanized Chinese regions Jing-Jin-Ji, the Yangtze River Delta and the Pearl River Delta. The analyses are based on classification of optical satellite imagery (Landsat TM/ETM+ and HJ-1A/B) over the past two decades. The images were first co-registered and mosaicked, whereupon GLCM texture features were generated and tasseled cap transformations performed to improve class separabilities. The mosaics were classified with a pixel-based SVM and a random forest decision tree ensemble classifier. Based on the classification results, two urbanization indices were derived that indicate both the absolute amount of urban land and the speed of urban development. The spatial composition and configuration of the landscape was analysed by landscape metrics. Environmental impacts were quantified by attributing ecosystem service values to the classifications and the observation of value changes over time. ivThe results from the comparative study between Shanghai and Stockholm show a decrease in all natural land-cover classes and agricultural areas, whereas urban areas increased by approximately 120% in Shanghai, nearly ten times as much as in Stockholm where no significant land-cover changes other than a 12% urban expansion could be observed. From the landscape metrics analysis results, it appears that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted in ecosystem service value losses of ca. 445 million US dollars in Shanghai, mostly due to a decrease in natural coastal wetlands. In Stockholm, a 4 million US dollar increase in ecosystem service values could be observed that can be explained by the maintenance and development of urban green spaces. Total urban growth in Shanghai was 1,768 km2 compared to 100 km2 in Stockholm. Regarding the comparative study of urbanization in the three Chinese regions, a total increase in urban land of about 28,000 km2 could be detected with a simultaneous decrease in ecosystem service values corresponding to ca. 18.5 billion Chinese Yuan Renminbi. The speed and relative urban growth in Jing-Jin-Ji was highest, followed by the Yangtze River Delta and the Pearl River Delta. The increase in urban land occurred predominately at the expense of cropland. Wetlands decreased due to land reclamation in all study areas. An increase in landscape complexity in terms of land-cover composition and configuration could be detected. Urban growth in Jing-Jin-Ji contributed most to the decrease in ecosystem service values, closely followed by the Yangtze River Delta and the Pearl River Delta. / <p>QC 20130610</p>

Remote Sensing (RS) technology provides valuable information about Earth by enabling an overview of the planet from above, making it a much-needed resource for many applications. Given the abundance of RS data and continued urbanisation, there is a need for efficient approaches to leverage RS data and its unique characteristics for the assessment and management of urban areas. Consequently, employing Deep Learning (DL) for RS applications has attracted much attention over the past few years. In this thesis, novel datasets consisting of satellite RS images over urban areas in Sweden were compiled from Sentinel-2 multispectral, Sentinel-1 Synthetic Aperture Radar (SAR) and Urban Atlas 2018 Land Use/Land Cover (LULC) data. Then, DL was applied for multiband and multiclass semantic image segmentation of LULC. The contributions of complementary spectral, temporal and SAR data and spectral indices to LULC classification performance compared to using only Sentinel-2 data with red, green and blue spectral bands were investigated by implementing DL models based on the fully convolutional network-based architecture, U-Net, and performing data fusion. Promising results were achieved with 25 possible LULC classes. Furthermore, almost all DL models at an overall model level and all DL models at an individual class level for most LULC classes benefited from complementary satellite RS data with varying degrees of classification improvement. Additionally, practical knowledge and insights were gained from evaluating the results and are presented regarding satellite RS data characteristics and semantic segmentation of LULC in urban areas. The obtained results are helpful for practitioners and researchers applying or intending to apply DL for semantic segmentation of LULC in general and specifically in Swedish urban environments.

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.

Dans les régions néo-tropicales, l’augmentation des taux de changements d’occupation et d’usages des sols et une forte déforestation durant la deuxième moitié du 20e siècle ont engendré une forte dégradation de l’environnement et une forte perte de biodiversité. Cette étude analyse les empreintes spatiales et les processus des changements d’occupation et d’usages des sols et de la déforestation pour le bassin versant du Grijalva-Usumacinta, l’un des plus importants du sud Mexique en matière hydrologique et de biodiversité, pour être confrontée aux discussions sur les changements forestiers émergents. Des cartes d’occupation et d’usages des sols de 1992, 2002 et 2007, dérivées d’images satellitaires et de photographies aériennes sont utilisées pour tester l’hypothèse d’un changement de trajectoires d’évolution à l’échelle régionale. Les probabilités et taux de changements ont été calculés pour les deux périodes 1992-2002 et 2002-2007, et les processus de changements dominants ont été identifiés. Les changements d’occupation et d’usages des sols sont complexes et ne peuvent s’expliquer par une histoire prédominante et linéaire de la déforestation. Deux des principaux résultats concernent (1) un taux anormalement élevé de dégradation des forêts primaires, équivalent à 1,7 fois la surface déforestée ; (2) les processus de déforestation se produisent principalement dans les forêts secondaires. Les activités agricoles, encouragées par les politiques publiques, sont les principaux moteurs de ces changements, parmi lesquelles le pâturage a le plus d'impact sur la déforestation. Les probabilités et taux de déforestation et de changement d’occupation et d’usages de sols ont stagné alors que la reforestation naturelle a augmenté. Bien que ces tendances sont essentielles pour le commencement de la transition forestière, la déforestation et la dégradation l'emportent bien sur la repousse de la végétation. / This study explores how to use techniques of prospective analysis in order to incorporate dynamic factors that put into risk the persistence of biodiversity into systematic conservation planning. Land use and cover change (LUCC) and climate change (CC) represent the main impacts and future threats to biodiversity and thus were the subject of analyses that provided information on prioritization for conservation actions. Since LUCC, CC and biodiversity loss, as well as the related socio-economic structures take place on a regional scale, this work is based on a large watershed in southern Mexico, the Grijalva-Usumacinta Basin. This basin is not only one of the most important areas for biological diversity, but is also renowned for its cultural complexity and hydrological importance and the multiple environmental and social problems that put biodiversity in peril. The main finding is that deforestation and forest degradation are the main LUCC processes and their high rates and strong future trends make it difficult to get to the point of forest transition in the near future, when deforestation and regeneration are balanced. Nevertheless, the scenario analysis shows that it is possible to influence LUCC trajectories in a substantial way in order to halt negative effects over biodiversity in the next decade. CC represents an additional threat to biodiversity difficult to evaluate, especially if the multiple synergistic effects between CC and LUCC are considered that could lead to much higher impacts. Anyway, the analysis showed that even until 2030 CC could have impacts on bioclimatic variables and species composition that could further hamper conservation efforts in the study area. Criteria for a proactive prioritization of sites for conservation are proposed based on scenarios of LUCC and CC. These criteria are used to identify “hot spots” (high probability of LUCC and severe CC impacts) and “refuges” (high probability of permanence and minor CC impacts). This joint analysis of CCUS and CC shows that there are differences between the conservation and the probable scenario; the effort needed to conserve the biodiversity contained in the priority sites in the conservation scenario is considerable less. The spatial pattern of hot spots and refuges of change is very similar across scenarios, despite the differences in absolute areas compromised by each. / Este estudio explora cómo aplicar técnicas de análisis prospectivos para incorporar factores dinámicos que ponen en riesgo la persistencia de biodiversidad en la planeación sistemática de la conservación (PSC). El cambio de cobertura y uso del suelo (CCUS) y el cambio climático (CC) representan los impactos y amenazas futuras más importantes para la biodiversidad, por lo que fueron escogidos como sujetos de análisis que proveen información para la priorización para acciones de conservación. Como el CCUS, el CC, la pérdida de biodiversidad, así como las estructuras socio-económicas relacionadas, ocurren a una escala regional, este trabajo está basado en una cuenca grande en el sur de México. La cuenca Grijalva-Usumacinta no sólo es una de las áreas más biodiversas en el mundo, también es reconocida por su complejidad cultural y su importancia hidrológica. A la vez se han documentado diversos problemas ambientales y sociales que ponen en peligro la persistencia de la biodiversidad. El resultado principal es que la deforestación y la degradación forestal son los procesos dominantes de CCUS y sus altas tasas y fuertes tendencias hacia el futuro vuelven difícil de llegar al punto de la transición forestal, en el cual la deforestación y la regeneración son balanceadas. Sin embargo, el análisis de escenarios muestra que es posible influenciar las trayectorias de CCUS de manera sustancial para detener los efectos adversos en la biodiversidad en la próxima década. El CC representa una amenaza adicional para la biodiversidad difícil de evaluar, especialmente si se consideran los múltiples efectos sinérgicos entre el CC y el CCUS que podrían hacer que el impacto sea mucho mayor. Aun así, el análisis mostró que hasta el 2030 el CC podría tener impactos en las variables bioclimáticas y la composición de especies que podrían dificultar más los esfuerzos de conservación en el área de estudio. Se proponen criterios para una priorización proactiva de la conservación son propuestos basados en escenarios de CCUS y CC. Estos criterios son usados para identificar focos rojos (alta probabilidad de CCUS e impactos de CC severos) y refugios (alta probabilidad de permanencia natural e impactos de CC menores). Este análisis conjunto de CCUS y CC muestra que hay diferencias entre el escenario de conservación y el escenario probable; el esfuerzo necesario para conservar la biodiversidad dentro de los sitios prioritarios es considerablemente menor. Los patrones espaciales de los focos rojos de cambio y los refugios son muy similares en los dos escenarios, a pesar de la diferencia absoluta de áreas en cada uno.

As mudanças climáticas globais, regionais e locais representam, na atualidade, uma das maiores preocupações da humanidade. Essas mudanças podem ocorrer tanto a partir de causas naturais quanto a partir de causas antrópicas. As áreas das cidades se caracterizam por apresentarem temperaturas mais elevadas quando comparadas com as áreas rurais. Essa anomalia térmica causa a formação de ilhas de calor e esse fenômeno é reconhecidamente importante em estudos de clima urbano. O objetivo do presente trabalho foi, através de técnicas do sensoriamento remoto, identificar e analisar as ilhas de calor do Município de Piracicaba, SP verificando sua sazonalidade, intensidade e morfologia. Para tanto foi necessário realizar uma análise climática regional e verificar a possibilidade do uso do algoritmo de transformação termal do software IDRISI 3.2 nas imagens do satélite Landsat 7. Para validar o algoritmo foram aplicados dois métodos de transformação de temperatura aparente de superfície. Para a análise climática regional foram estudados os principais elementos climáticos do Município de Piracicaba, SP utilizando-se de dados da Estação Meteorológica da ESALQ/USP entre os anos de 1950 e 2005 e estes foram correlacionados com variáveis da urbanização. Concluiu-se, com os dados encontrados, que os elementos temperatura, precipitação, umidade relativa e evaporação tiveram tendência de aumento no período estudado e todos eles foram classificados como tendências climáticas. A temperatura apresentou tendência de aumento mais acentuada e se correlacionou positivamente com o aumento da urbanização. O algoritmo de transformação do software IDRISI 3.2 para o satélite Landsat 7 foi validado, sendo uma importante ferramenta para a utilização de imagens de melhor resolução. As ilhas de calor mais intensas do verão são representadas por locais com excesso de material de construção civil e pouca ou nenhuma área verde. A diferença entre a área urbana e a área rural da cidade ultrapassou 16°C no verão. O Parque da Rua do Porto é uma ilha de frescor e exerce um "efeito oásis" no centro e nos bairros vizinhos. O perfil das ilhas de calor do Município de Piracicaba não segue aquele delimitado por OKE (1974). As ilhas de calor variam sazonal e espacialmente e a intensidade destas, ao longo das estações do ano, está intimamente relacionada com a sazonalidade da cultura da cana-deaçúcar. As ilhas de calor da época da entressafra são, em média, 3.5°C mais intensas que as da época da safra. Por fim, pode-se afirmar que o uso e a cobertura do solo rural e urbano é um dos grandes agentes modificadores do clima local e regional. / Global, regional and local climate changes represent one of the greatest concerns of humanity. Climate changes can occur through natural or anthropogenic causes. Urban areas usually present higher temperatures than rural areas. This thermal effect is called "heat-island phenomenon" and has great importance on urban climate studies. In the present work, we identified and analyzed the heat-islands from Piracicaba, São Paulo using remote sensing techniques. The heat-islands were analyzed according to its seasonality, intensity and morphology using images from Landsat 7 satellite. We performed analysis on regional climate changes and investigated the use of the IDRISI thermal algorithm to convert Landsat 7 infrared thermal data on land surface temperature (LST). In order to transform Landsat 7 infrared thermal data we used two mathematical methods. Climate changes were analyzed by monitoring the climate elements for long periods of time, enabling the visualization of directional or periodical regional changes. The main climate elements were studied using data from ESALQ meteorological station for the last 55 years (1950-2005). Temperature, relative humidity, evaporation and precipitation variation were found to be correlated with urban growth parameters. The results indicated that temperature, precipitation, relative humidity and evaporation increased during the studied period and have been classified as "climate trends". The temperature presented the more accentuated trend of increase and was positively correlated with the growing urbanization. The software IDRISI 3.2 can be used with Landsat 7 high resolution images, being a useful and rapid tool to study urban heat islands. The most intense summer heatislands were represented by regions with higher amounts of constructed areas and almost any green area. In fact, during the summer the difference between the urban and rural areas was greater than 10°C. The Rua do Porto park was identified as a fresh-island and showed the "oasis effect" to the Center and neighbouring regions. Heat-islands varied according to the season and space and its intensity is intimately related to the sugar-cane seasonality. During the intercrop period the heat-islands were 3.5°C more intense than during the crop period. In conclusion land cover and land use affect local and regional climates.

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