Establishing wetland gains and losses, delineating wetland boundaries, and determining their vegetative composition are major challenges that can be improved through remote sensing studies. In this study, we used the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to separate wetlands from uplands in a study of 870 locations on the Virginia Coastal Plain. We used the first five bands from each of two ASTER scenes (6 March 2005 and 16 October 2005), covering the visible to the short-wave infrared region (0.52-2.185υm). We included GIS data layers for soil survey, topography, and presence or absence of water in a logistic regression model that predicted the location of over 78% of the wetlands. While this was slightly less accurate (78% vs. 86%) than current National Wetland Inventory (NWI) aerial photo interpretation procedures of locating wetlands, satellite imagery analysis holds great promise for speeding wetland mapping, lowering costs, and improving update frequency. To estimate wetland vegetation composition classs of the study locations, we generated a Classification and Regression Tree (CART) model and a Multinomial Logistic Regression (logit) model, and compared their accuracy in separating woody wetlands, emergent wetlands and open water. The overall accuracy of the CART model was 73.3%, while the overall accuracy of the logit model was 76.7%. Although the CART producer's accuracy (correct category classification) of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), we obtained the opposite result for the woody wetland category (68.7% vs. 52.6%). A McNemar test between the two models and NWI maps showed that their accuracies were not statistically different. We conducted a sub-pixel analysis of the ASTER images to establish canopy cover of forested wetlands. The canopy cover ranged from 0 to 225 m2. We used visble-near-infrared ASTER bands, Delta Normalized Difference Vegetation Index, and a Tasselled Cap transformation in an ordinary linear regression (OLS) model. The model achieved an adjusted-R2 of 0.69 and an RMSE of 2.73% when the canopy cover is less than 16%. For higher canopy cover values, the adjusted-R2 was 0.4 and the RMSE was19.79%. Taken together, these findings suggest that satellite remote sensing, in concert with other spatial data, has strong potential for mapping both wetland presence and type. / Ph. D.

L’Arganier [Argania spinosa (L.) Skeels] est un arbre de la famille des Sapotacées, endémique du sud-ouest marocain. C’est un arbre multi-usages, qui constitue une ressource primordiale pour les populations de cet espace semi-aride et aride du Maroc. Il constitue la clef-de-voûte de l’agro-écosystème traditionnel de l’arganeraie reposant sur un équilibre entre ressources et exploitation humaine, et joue également un rôle important dans la lutte contre la désertification et l’érosion. Actuellement, la menace de dégradation de l’arganeraie est une préoccupation majeure aussi bien pour la population que pour les scientifiques. On assiste en effet depuis plusieurs décennies à une diminution du couvert arboré, à la fois en surface occupée et en densité d’arbres. Face à cette préoccupation, nous avons étudié l’espace multidimensionnel de l’arganeraie en cherchant à identifier les principales caractéristiques de cet espace, ainsi que les facteurs responsables de sa dégradation. Ensuite, nous avons dévoilé l’originalité de cet espace à partir de son organisation sociale et spatiale, ainsi que le mode de fonctionnement et de gestion de ce territoire. Dans la deuxième partie nous avons montré l’apport de la télédétection spatiale et des systèmes d’information géographique pour la caractérisation de l’occupation du sol et l’identification des changements à partir d’un suivi diachronique, en utilisant une série d’images SPOT, Landsat, Google Earth, Ikonos. Nous avons également testé la possibilité d'évaluer la densité des arganiers à partir des images à haute résolution spatiale Ikonos et Google Earth. Nous concluons à la nécessité d’un suivi de ce territoire afin de pouvoir évaluer les changements et prendre les mesures d’aménagement et de protection nécessaires / The Argan [Argania spinosa (L.) Skeels] is a species of tree endemic to the calcareous semi-desert Sous valley of southwestern Morocco. It is the sole species in the genus Argania (family of Sapotaceae). It is a multi-purpose tree, and the main resource provider for the population of this semi-arid and arid area (source of forage, oil, timber and fuel). Argan is the keystone species of the traditional agro-ecosystem of the Berber society, ensuring a meta-stable equilibrium between resource availability and anthropic use; it plays a major role in preventing erosion and desertification damages.Currently, in spite of the Biosphere Reserve label attributed by UNESCO in 1998, the threat of degradation of the sparse Argan forest is a main concern for both local population and scientists. Since several decades, a decrease of extension area of the species and of tree density has been observed. According to this preoccupation, we have studied the multidimensional space of the Argan forest, in view of identifying its main features and the potential drivers of degradation processes. Then the originality of this area has been demonstrated through the assessment of its social and spatial organization, and of land-use and management practices.In the second part, we have shown the possible use of remotely sensed data and of Geographic Information Systems for surveying land-use/land-cover and for monitoring changes through a multi-temporal analysis of satellite images: SPOT, Landsat, Ikonos and Google Earth imagery. The evaluation of tree density has been performed through object-oriented classification of high spatial resolution satellite imagery (Ikonos, Google Earth). In conclusion, we recommend the effective use of a monitoring system to follow environmental changes in the Argan tree area, and to produce the detailed information needed for implementation of management and conservation strategies ensuring a sustainable development of the area.

Land degradation is one of the most important drivers of landscape change around the globe. This dissertation examines land use-land cover change within a mosaic landscape in Eastern Terai, India, and shows evidence of anthropogenic factors contributing to landscape change. Land use and land cover change were examined within the Alipurduar Subdivision, a representative of the Eastern Terai landscape and the Jaldapara Wildlife Sanctuary, a protected area nested within Alipurduar through the use of multi-temporal satellite data over the past 28 years (1978 - 2006). This study establishes the potential of remote sensing technology to identify the drivers of landscape change; it provides an assessment of how regional drivers of landscape change influence the change within smaller local study extents and provides a methodology to map different types of grassland and monitor their loss within the region. The Normalized Difference Vegetation Index (NDVI) and a Normalized Difference Dry Index (NDDI) were found instrumental in change detection and the classification of different grasslands found inside the park based on their location, structure, and composition. Successful spectral segregation of different types of grasslands and their direct association with different grassland specialist species (e.g., hispid hare, hog deer, Bengal florican) clearly showed the potential of remote sensing technology to efficiently monitor these grasslands and assist in species conservation. Temporal analysis provided evidence of the loss of dense forest and grasslands within both study areas with a considerably higher rate of loss outside the protected area than inside. Results show a decline of forest from 40% in 1978 to 25% in 2006 across Alipurduar. Future trends project forest cover and grassland within Alipurduar to reduce to 15% and 5%, respectively. Within the Alipurduar, deforestation due to growth of tea industry was the primary driver of change. Flooding changed the landscape, but more intensely inside the wildlife preserve. Change of the river course inside Jaldapara during the flood of 1968 significantly altered the distribution of grassland inside the park. Unless, the direction of landscape change is altered, future trends predict growth of the tea industry within the region, increased forest loss, and homogenization of the landscape.

An integrating approach, including knowledge about whole systems of processes, is essential in order to reach both development and environmental protection goals. In this thesis Geographical Information Systems (GIS) are suggested as a tool to realise such integrated models. The main hypothesis in this work is that several natural technical and social systems that share a time-space can be compared and analysed in a GIS. My first objective was to analyze how GIS can support research, planning, and, more specifically, bring a broad scattering of competence together in an interdisciplinary process. In this process GIS was ivestigated as a tool to achieve models that give us a better overview of a problem, a better understanding for the processes involved, aid in foreseeing conflicts between interests, find ecological limits and assist in choosing countermeasures and monitor the result of different programs. The second objective concerns the requirement that models should be comparable and possible to include in other models and that they can be communicated to planners, politicians and the public. For this reason the possibilities to communicate the result and model components of multidimensional and multi-temporal data are investigated. Four examples on the possibilities and problems when using GIS in interdisciplinary studies are presented. In the examples, water plays a central role as a component in questions about development, management and environmental impact. The first articles focus on non-point source pollutants as a problem under growing attention when the big industrial and municipal point sources are brought under control. To manage non-point source pollutants, detailed knowledge about local conditions is required to facilitate precise advices on land use. To estimate the flow of metals and N(itrogen) in an area it is important to identify the soil moisture. Soil moisture changes over time but also significantly in the landscape according to several factors. Here a method is presented that calculate soil moisture over large areas. Man as a hydrologie factor has to be assessed to also understand the relative importance of anthropogen processes. To offer a supplement to direct measurements and add anthropogen factors, a GIS model is presented that takes soil-type, topography, vegetation, land-use, agricultural drainage and relative position in the watershed into account. A method to analyse and visualise development over time and space in the same model is presented in the last empirical study. The development of agricultural drainage can be discussed as a product of several forces here analyzed together and visualized with help of colour coded "Hyper pixels" and maps. Finally a discussion concerning the physiological and psychological possibilities to communicate multidimensional phenomena with the help of pictures and maps is held. The main conclusions in this theses are that GIS offer the possibilities to develop distributed models, e.g., models that calculate effects from a vide range of factors in larger areas and with a much higher spatial resolution than has been possible earlier. GIS also offer a possibility to integrate and communicate information from different disciplines to scientists, decision makers and the public. / <p>Diss. (sammanfattning) Umeå : Umeå universitet, 1993, härtill 6 uppsatser.</p> / digitalisering@umu

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The monitoring of the use and coverage is very important when studying determined regions, just because it helps knowing the environmental reality and contributes to solve problems that can probably appear. This research was done from the images of ALOS and LANDSAT satellites. Its main objective was to have a multi-temporal analysis of Arroio Grande micro watershed, central region of Rio Grande do Sul. The specific purposes were to identify and to quantify the different classes of land use found in this micro watershed along the 1987, 1998, 2002, 2005, 2007 and 2009 periods, as well as cross the land use information to show the forest coverage changes during the 22 years of analysis. The software SPRING 5.1.7 was employed to classify the supervised images through Bhattacharya, a sorter algorithm, and the map spatial analysis was done through the Spatial Language of Algebraic Geoprocessing program with the same computational application. The classes of land use as forest, field, agriculture, irrigated agriculture, exposed soil and water layer were observed in the images of each year in this analysis. These classes were utilized in the spatial analysis of the forest coverage in which forest monitoring parameters have been defined (forest maintenance and regeneration, deforestation). In this research the principal results that have been noticed were the increase of 17,98% on the distributed forest coverage, mainly in the areas of bigger declination, and the reduction of 16,32% on the field area. The analysis of the spatial forest coverage has presented stability with the landscape, in a gradual progression, because the area of forest maintenance, found in these 22 years, was 12.252,60ha, the forest regeneration was 4.389,12ha and only 1.853,82ha of deforested area. / O monitoramento do uso e cobertura da terra faz-se importante no estudo de determinadas regiões, pois auxilia no conhecimento da realidade ambiental e contribui na busca por soluções de problemas que possam se apresentar. A partir do uso de imagens dos satélites, ALOS e LANDSAT, foi realizada esta pesquisa com o objetivo principal de fazer uma análise multitemporal na microbacia do Arroio Grande, região central do Rio Grande do Sul, cujos objetivos específicos foram: Identificar e quantificar as diferentes classes de uso da terra encontradas na microbacia nos períodos de 1987, 1998, 2002, 2005, 2007 e 2009; além de cruzar as informações de uso da terra, evidenciando a cobertura florestal que sofreu alterações no decorrer dos 22 anos de análise. Para tanto, foram utilizados o software SPRING 5.1.7 para a classificação supervisionada das imagens, com a adoção do algoritmo classificador Bhattacharya, e a análise espacial dos mapas com a programação LEGAL do mesmo aplicativo computacional. Para esta análise, foram observadas as classes de uso do solo: floresta, campo, agricultura, agricultura irrigada, solo exposto e lâmina d água, nas imagens de cada ano. Estas classes foram empregadas na análise espacial da cobertura florestal em que foram definidos parâmetros para o monitoramento florestal (manutenção florestal, regeneração florestal e desmatamentos). Os principais resultados notados, nesta pesquisa, foram o aumento de 17,98% na cobertura florestal distribuída, principalmente, nas áreas de maiores declividade, e a redução de 16,32% sobre a área de campo. Quanto à análise espacial da cobertura florestal, esta mostrou-se em estabilidade com a paisagem, e em gradual progressão, pois a área de manutenção florestal encontrada, nestes 22 anos, foi de 12.252,60 ha, a regeneração florestal foi de 4.389,12 ha e apenas 1.853,82 ha de área desmatada.