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Mapping and monitoring disturbances in vegetation over large areas demand reliable approaches and accurate end-user maps. Methods and algorithms have been developed to meet satisfactory disturbance map accuracies, and the combination of multiple approaches has shown promise as a reliable alternative to any single method. However, extracting meaningful disturbance information from these combined methods is still challenging. Data variance from environmental conditions and disturbance drivers leads to spatial-temporal heterogeneity in land surfaces over large areas, which results in mapping errors. We evaluated the effectiveness of ensemble classification and data-driven regionalization for mapping vegetation disturbances at a broad scale. Using Google's Earth Engine cloud computing platform, our ensemble approach combines multispectral LandTrendr outputs reflecting preliminary disturbance information in a Random Forest model to map disturbances in Minas Gerais, Brazil. We then applied an unsupervised clustering technique to perform data-driven regionalization of our study area using several sources of environmental and anthropogenic information and analysed gains and losses in map accuracies. Our results indicated gains in accuracy by the ensemble method compared to non-ensemble methods of disturbance mapping, which ranged from 7.3 to 29.9% in overall accuracy at the 5% significance level. Data-driven regionalization addressed complexities arising from variability in vegetation types, local climate, and topography across our study area, identifying climate and seasonal metrics as important variables for reducing uncertainties in vegetation disturbance maps. The integration of these techniques has revealed significant potential for increasing map accuracy and has provided important insights into the development of disturbance mapping methods in heterogeneous environments. [ABSTRACT FROM AUTHOR]

Applications of L-band SAR data to map deforestation are generally based on the assumption that undisturbed forests consistently exhibit higher radar backscatter than deforested areas. In this Letter we show that depending on the stage of the deforestation process (slashing, burning and terrain clearing), this assumption is not always valid, and deforested areas may display a stronger radar return backscatter than primary forest. The analysis of multitemporal SAR images, supported by several Landsat Thematic Mapper (TM) images and field knowledge, showed that wood materials left following the deforestation practices function as corner reflectors, causing an initial increase in the radar backscatter, which then subsequently decreases over time as the debris on these fields are removed. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) has been associated with vegetation productivity and evapotranspiration in mathematical models. At a regional level LAI can be estimated with enough accuracy through spectral vegetation indices (SVIs), derived from remote sensing imagery. However, there are few studies showing LAI-SVI relationships in subtropical regions. The aim of this work was to examine the relationship between LAI and SVIs in a subtropical rural watershed (in Piracicaba, State of São Paulo, Brazil), for different land covers, and to use the best relationship to generate a LAI map for the watershed. LAI was measured with a LAI-2000 instrument in 32 plots on the field in areas of sugar cane, pasture, corn, eucalypt, and riparian forest. The SVIs studied were Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI), calculated from Landsat-7 ETM+ data. The results showed LAI values ranging from 0.47 to 4.48. LAI-SVI relationships were similar for all vegetation types, and the potential model gave the best fit. It was observed that LAI-NDVI correlation ( r 2 =0.72) was not statistically different from LAI-SR correlation ( r 2 =0.70). The worst correlation was obtained by LAI-SAVI ( r 2 =0.56). A map was generated for the study area using the LAI-NDVI relationship. This was the first LAI map for the region. [ABSTRACT FROM AUTHOR]

Attempts to map vegetation types, especially pasture, from satellite sensor data in tropical and sub-tropical regions very often have limited success. This study analyses the accuracy of two classifications of Landsat Thematic Mapper (TM), with the aim of distinguishing the pastures from other vegetation classes in a meso-scale basin (12 400 km[sup 2], Piracicaba basin, Brazil). The initial classification is based on non-supervised clustering of the images. The delimited classes are interpreted and merged by comparison with standard spectra from NASA. The second classification is a parallelepiped partition based on the merged clusters issued from the first one. The two classifications are compared by validation with 287 random field observations selected within the whole catchment. The results are discussed, analysing the spectral behaviour variability of the pasture class. [ABSTRACT FROM AUTHOR]

Discusses about fraction image of vegetation in Mato Grosso, Brazil. Characteristics of fraction image; Color composites of fraction image; Illustration of composite fraction image.