Zonage du Bresil a partir d'une serie temporelle d'images modis.

Zoneamento do Brasil a partir de uma série temporal de imagens MODIS . A cartografia das paisagens envolvem geralmente a combinação de informações ambientais e informações sobre as atividades humanas. A qualidade da carta de paisagem resultante depende fortemente da expertise e do método utilizado, assim como da qualidade dos dados que foram usados na sua elaboração. As séries temporais das imagens de satélite aportam uma visão objetiva do território a diferentes datas. Estas imagens podem ser segmentadas para estratificar o espaço em zonas radiometricamente homogêneas. O objetivo deste trabalho é testar este método de estratificação a diferentes escalas espaciais, no Brasil e na região do estado do Maranhão e avaliar as estratificações de forma não supervisionada. Para tanto, uma segmentação orientada à objeto foi realizada utilizando-se o software eCognition a partir de valores dos índices de vegetação EVI (Enhaced Vegetation Index) e de índices de textura advindos de uma série temporal de imagens MODIS com resolução espacial de 250m. Diferentes variáveis radiométricas e diferentes escalas de segmentação foram testadas e avaliadas através de dois indicadores estatísticos. A segmentação obtida foi, então, comparada visualmente aos zoneamentos existentes (GAEZ da FAO e zoneamento agroecológico da Embrapa)

Decadal Land Surface Phenology and Water Quality in the Headwaters Illinois River Watershed

Over 25 percent of the world’s population either lives on or obtains water from karst aquifers. The complex interactions between subsurface karst geologic features, the constant motion of the plant life cycle, and significant water resource demand all suggest the need to better define those interactions. The relationship of historical land surface phenology and water quality in karst topography were investigated in the Headwaters Illinois River watershed in Northwest Arkansas (NWA). This area represents high vulnerability to surface water and groundwater contamination, with both natural and anthropogenic processes such as over application of broil litter for enhanced cattle browse, affecting groundwater quality. Land surface phenology patterns influenced by these processes were identified using Landsat satellite imagery and object-based image analysis (OBIA). A normalized difference vegetation index (NDVI) time series was produced using Google Earth Engine for all passes over the study area that meet atmospheric and data quality criteria over two decades from 1999 to 2018. Analysis of NDVI and ancillary data over time allowed insight into vegetation health norms, deviation from those norms, and human impact upon regional vegetation. OBIA techniques were used to segment vegetation index time series pixels into polygons based on adjacency and similarity. Resulting polygons were categorized using an unsupervised clustering approach, and were labeled based on visual and expert interpretation of the study area. The relation of the image analysis results to groundwater quality was determined using data organized by hydrologic catchments within the study area. Comparison of the decadal water quality data and NDVI image analysis resulted in meaningful temporal patterns within the datasets but showed a near 0 slope for NDVI and water quality metrics. Future LSP studies should consider areas with greater spatial and temporal availability of water quality metrics and variable surface/groundwater interactions

Object-based delineation of homogeneous landscape units at regional scale based on MODIS time series

International audienceLandscapes can be described by seasonal and spatial patterns linked to vegetation type and phenology, environmental conditions, and human activities. The objective of this work is to propose and test an approach for delineating homogeneous landscape units at a regional scale by using only Earth observation data. We used MODIS (Moderate Imaging Spectroradiometer) images from 2007 to 2011, acquired over the whole continental French territory at 250 m spatial resolution. The data set includes time series of the Enhanced Vegetation Index (EVI) and time series of five Haralick texture indices. A principal components analysis (PCA) allowed us to choose the most representative indices (spectral and textural) and dates to be used in the region-growing segmentation. Different combinations of input data, as well as different segmentation parameters, were tested and compared using unsupervised evaluation methods. These methods were used to analyze the radiometric homogeneity of the regions and the radiometric disparity between regions when changing the homogeneity criterion of the segmentation. The best segmentation results obtained included three EVI images, together with three images of the texture 2nd moment, corresponding to the average of the months of April, July and December from 2007 to 2011. The optimum homogeneity criterion for the region-growing segmentation using this combination of variables was 15. We believe this method is applicable at other scales and other data sets for vegetation and biodiversity studies, and for habitat mapping. (C) 2014 Elsevier B.V. All rights reserved