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R ESU M OO estudo da umidade do solo é fundamental não só para a determinação da resiliência de ecossistemas e sua recuperação, mas também na modelagem da relação água-vegetação-atmosfera. Na aquisição dessas informações o sensoriamento remoto perfaz uma ferramenta importante e de potencial adequado para monitoramento e mapeamento. Visando à espacialização de índices relacionados à umidade, vários métodos têm sido propostos, embora sua aplicação ainda seja limitada. Neste trabalho se aplicou o modelo de índice de umidade do solo (IUS) cujos objetivos foram: espacializar o IUS, estabelecer graus de desertificação, delimitar a área em processo de desertificação e verificar possíveis relações do IUS com parâmetros de água no solo. Na aplicação deste modelo se utilizaram, como dados de entrada, o NDVI (índice de vegetação da diferença normalizada) e a LST (temperatura da superfície) e se observou que o IUS representado pela média dos valores desses índices pode ser empregado na determinação do grau de degradação da superfície e para gerar classificação legendada, discriminando vários níveis de degradação ambiental. Constatou-se também que não houve relação direta do IUS com parâmetros físicos de retenção de umidade do solo. Desta forma, o sensoriamento remoto mostrou ser uma ferramenta significativa na avaliação de índices de umidade do solo em áreas degradadas tal como para delinear a dinâmica de borda em núcleo de desertificação.Palavras-chave: índice de umidade do solo, NDVI, Seridó, desertificação Spatial distribution of soil moisture using land surface temperature and vegetation indices A B ST R A C TThe study of soil moisture is important in determining the resilience of ecosystems and their recovery, as well as in the modeling of water-vegetation-atmosphere relationship. Remote sensing is an important tool for the acquisition, mapping and monitoring soil moisture through the surface temperature and vegetation indices. For the soil moisture content assessment, several methods have been proposed, however its application is still limited. In this work the soil moisture index (SMI) was applied and modeled with the objectives: to establish and delineate areas with different levels of desertification through SMI mapping and to map the dynamic of border, as well as to verify possible relationships betweem SMI and soil water parameters. In the application of this model as input data was used: NDVI (normalized difference vegetation index) and LST (land surface temperature). It was observed that SMI accessed by the average of the SMI derived by NDVI and LST can be used in the determination of soil surface degradation and in the production of maps showing different levels of this degradation. It was also verified, that there was no direct relationship between SMI and physical parameters of soil moisture content. Remote sensing showed to be an important tool in the evaluation of soil moisture indices in degraded areas and to delineate the border effect in this desertification nucleus.

In the analysis of trophic state of the water body is fundamental to know chlorophyll-a concentration. Thus, this work has as main aim to determinate and to assess the behavior of chlorophyll-a in the Itaparica reservoir, São Francisco river. This way, we used Landsat-TM imagery, in which it was used bands from 1 to 5 and 7. The algorithm used was written in LEGAL/SPRING 5.2. From the chlorophyll-a result was held slicing the water body in six concentration classes. As observed by histogram, the minimum value of Chl-a was < 1 µg/L and the highest was 249.5 µg/L. The classes that had the biggest area were Classe 01 (0-5 µg/L) with 27.4%, followed by Classe 02 (5-10 µg/L) with 24.6% of the total area of the study area. Through graphical analysis of points located along the reservoir it was possible to verify that chlorophyll concentration augmented from fluvial to lacustrine region and from the contact of streams with reservoir. In the next studies there is a need to validate the values with field data in order to verify the mapping accuracy in this reservoir, taking into account the day and also the transit time of the sensor.

Solar Thermal Technology for the generation of electricity in large scale has been a reality in the world since the 1980s, when the first large-sized solar plants in the United States were introduced. Brazil presents great potential for the development of large-scale projects, although it is noted that the main barriers for the insertion of this technology in Brazilian market are the lack of incentives and goals and associated costs. In a way to contribute to the insertion of solar thermal technology in Brazil, this paper presents a macro-spatial approach, based on the use of Multiple-Criteria Decision Analysis and Geoprocessing, for the location of solar thermal power plants. The applied methodology for Pernambuco, located in the Northeast Region of Brazil, considered the implantation of parabolic trough solar power plant of 80 MW, operating only in solar mode, without heat storage. Based on performed analysis, it was confirmed that Pernambuco presents great potential for the installation of solar power plants, especially in the backlands of Pernambuco. Performed validations in the model demonstrate that the methodology attended the objective once the consistence between the assigned weights to the thematic layers, individually, and the final Map of site suitability were evidenced.

RESUMOO estudo de parâmetros biofísicos como o Índice de Vegetação da Diferença Normalizada (NDVI), albedo e temperatura da superfície (LST), aplicado a ecossistemas, tem sido relevante para o entendimento de mudanças relacionadas à degradação do meio ambiente. Algumas alterações que provocam desequilíbrio de interações ecológicas em ecossistemas, como o desmatamento, a mineração, a agricultura inadequada e o superpastejo, entre outros, estão interrelacionadas. Para avaliação de mudanças temporais relacionadas à degradação do ecossistema caatinga obtiveram-se as imagens da diferença de três parâmetros: NDVI, albedo e temperatura da superfície, para os anos de 1985 e 2001 (estação seca), utilizando-se imagens TM. Este estudo foi aplicado à bacia do rio Brígida onde há uma exploração intensa dos recursos naturais, em que os resultados mostram aumento na temperatura da superfície, diminuição do NDVI e pouca variação no albedo da superfície evidenciando, assim, que entre os anos de 1985 e 2001 houve avanço na degradação dos recursos naturais, nesta bacia.Palavras-chave: temperatura da superfície, albedo, índice de vagetação, degradação da caatinga, modelagem de dados Biophysical parameters in the detection of changes in soil cover and use in watersheds ABSTRACTThe study of biophysical parameters such as NDVI, albedo and surface temperature has been reported as important for the understanding of land degradation changes of ecosystems. Degradation in ecosystems is related to the inadequate use of the environmental resources including deforestation, mining, inadequate agriculture, overgrazing, among others, that cause imbalance of ecological interactions. For multitemporal evaluation of changes related to the degradation of the 'caatinga' ecosystem three parameters were used with TM images: NDVI, albedo and surface temperature, in two different dates, 1985 and 2001 (dry season). This study was applied in the Brígida river basin where there is a great intensification in the exploration of the natural resources. The results show an increase in the surface temperature, decrease of NDVI and little variation in the surface albedo, between the two dates. These results also show an increase in the degradation of the natural resources in the basin of the Brígida river.

This article presents a hybrid approach for texture based image classification using the gray-level co-occurrence matrices (GLCM) and self-organizing map (SOM) methods. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image database are processed to create the training data set for a SOM neural network. The SOM model organizes and extracts prototypes from processed GLCM matrices. This paper proposes a novel strategy to index match scores by searching through prototypes. A benchmark data set is used to demonstrate the usefulness of the proposed methodology. The evaluation of performance is based on accuracy in the framework of a Monte Carlo experience. This approach is compared with several classifiers in Li et al [1]. The experimental results on the Brodatz texture image database demonstrate that the proposed method is encouraging with an average successful rate of 97%.

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