Abstract:The need to monitor the Earth's surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together. OPEN ACCESSRemote Sens. 2011, 3 2474

The topology and geometry of the drainage networks have contributed substantially to geomorphology and hydrology studies, including modern concepts of landscape evolution. This work aims at presenting a methodology for automated extraction of drainage basins and sub-basins, calculating their morphometric attributes and grouping them in accordance to their similarities. The methodology can be subdivided into four stages: (a) elaboration of the hydrologically corrected DEM; (b) delimitation catchment boundaries from digital elevation model (DEM) according to Strahler's drainage network order; (c) determination of the drainage-basin morphometric attributes, and (d) multivariate analysis (factor analysis and cluster analysis). In this paper algorithms for drainage network extraction and catchment boundaries from DEMs, as well as their most pertinent problems, are presented. The DEM preparation included pit filling, stream burning and the calculation of flow direction and flow accumulation grids. The Principal Component Analysis reduced the morphometric attributes of the drainage basins in three factors, with high percentage of the original variance. Cluster analysis defined seven classes of basins with typical morphological patterns. The results suggest that it is viable to apply the same sequence of procedures in other geographic areas.

ABSTRACT. Savannas NDVI sãoúteis na diferenciação dos tipos de vegetação. A eficiência da metodologia tem sido provada para delimitação regional das fisionomias de savana, mesmo considerando a baixa resolução espacial de 250m do sensor MODIS e a alta mistura espectral.Palavras-chave: Cerrado, detecção de mudança, análise multitemporal, processamento digital de imagem.

Consistent multi-temporal images are necessary for accurate landscape change detection and temporal signatures analysis. Orbital images have a difficultyto maintain a temporal information precision due to several interferences that generate missing data. In this paper is developed a program in C++ languagefor denoising MODIS temporal signatures considering two-phase scheme for removing impulse and white noise. In the first phase, the median filter is used to identifyimpulse noise. In the second phase, the Noise-Adjusted Principal Components (NAPC) transform is applied to eliminate white noise. Because they are two complementarymethods, there is high performance in removing noise. The restored NDVI (Normalized Difference Vegetation Index) signatures showed a significant improvementproviding a time series dataset that can be used to identify and classify the vegetation physiognomic types.

Os mapas auto-organizáveis (SOFM) consistem em um tipo de rede neural artificial que permite a conversão de dados de alta dimensão, complexos e não lineares, em simples relações geométricas com baixa dimensionalidade. Este método também pode ser utilizado para a classificação de imagens de sensoriamento remoto, pois permite a compressão de dados de alta dimensão preservando as relações topológicas dos dados primários. Este trabalho objetiva desenvolver uma metodologia eficaz para a utilização de mapas auto-organizáveis na detecção de mudanças. No presente estudo o SOFM é utilizado para a classificação não supervisionada de dados de sensoriamento remoto, considerando os seguintes atributos: espaciais (x, y), espectrais e temporais. O método é empregado na região oeste da Bahia, que teve recentemente um aumento significativo em monoculturas. Testes foram realizados com os parâmetros do SOFM com o objetivo de refinar o mapa de detecção demudanças. O SOFM possibilita uma melhor seleção de células e dos correspondentes vetores de peso, que mostram o processo de ordenação e agrupamento hierárquicodos dados. Esta informação é essencial para identificar mudanças ao longo do tempo. Um programa em linguagem C ++ do método proposto foi desenvolvido. ABSTRACT. Self-organizing feature maps (SOFM) consist of a type of artificial neural network that allows the conversion from high-dimensional data into simple geometric relationships with low-dimensionality. This method can also be used for classification of remote sensing images because it allows the compression of high dimensional data while preserving the most important topological and metric relationships of the primary data. This paper aims to develop an effective methodology forusing self-organizing maps in change detection. In this study, SOFM is used for unsupervised classification of remote sensing data, considering the following attributes: spatial (x and y), spectral and temporal. The method is tested and simulated in the western region of Bahia that has observed a significant increase in mechanized agriculture. Tests were performed with the SOFM parameters for the purpose of fine tuning a change detection map. The SOFM provides the best selection of cell and corresponding adjustment of weight vectors, which show the process of ordering and hierarchical clustering of the data. This information is essential to identify changes over time. All algorithms were implemented in C++ language.Keywords: unsupervised classification; land cover; multitemporal analysis; remote sensing