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Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform. [ABSTRACT FROM AUTHOR]

The Brazilian Amazonian rain forests are under imminent threat of serious degradation and ultimately deforestation. Human activities such as selective logging are an important cause. Selectively logged locations are difficult to detect from medium-resolution Landsat images, due to their relatively small sizes and subtle spatial patterns. Spectral linear unmixing provides an effective tool for the purpose. The orientation of geometrically corrected images, however, artificially introduces zero-reflectance background pixels. These change the variance–covariance structure of the image bands and hinder the identification of pure endmembers. In this study, we compare image cropping and image rotation as two alternative approaches. Selectively logged forests were detected in northern Rondônia state, north-western Mato Grosso state and south-eastern Amazonas state in Brazil by applying spectral unmixing. The study shows that image rotation is a better approach as it preserves the image extent and thus provides information on forest degradation over a wider region. Spatial statistical analysis of the detected locations shows strong clustering within the study area. We conclude that the endmembers used in this study represent basic components of a degraded forest environment. As spectral unmixing of remote-sensing images avoids collection of field data, it may broadly be applied towards other Amazonian regions as well. [ABSTRACT FROM AUTHOR]

Several areas of land degradation are present in the northeastern part of Brazil, resulting primarily from old practices of vegetation clearing and burning for subsistence farming and grazing. However, no reliable information is currently available on the extent of these areas and whether they are expanding over time. Taking the Gilbues region as an example, this study shows the potential for obtaining this information using multitemporal dual-season Landsat Thematic Mapper (TM) imagery. We used digital image processing, which involved the enhancement of soil-fraction images by segmentation/region classification and Normalized Difference Index (NDI) techniques. The results show highly degraded soils covering an almost continuous area of approximately 600 km2. The data also show that, over the past two decades, land degradation has been concentrated mainly along and in the heads of drainage systems, a pattern that indicates removal of riparian forests, thereby compounding the serious problem of sediment loading in the streams in the area. We show that satellite remotely sensed data can provide valuable information in support of environmental policies for northeast Brazil. [ABSTRACT FROM AUTHOR]