2016
DOI: 10.1590/1809-4392201505504
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High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data

Abstract: Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visua… Show more

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Cited by 285 publications
(251 citation statements)
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“…2. Surface cover was obtained from the digital Terra Class classification data (Almeida et al, 2016). This database contains 15 vegetation classes, such as forest, hydrology, urban areas, and several deforestation classes, including clean and dirty pasture, deforested areas, and exposed soil.…”
Section: Rainfall Sensitivity To Surface Covermentioning
confidence: 99%
“…2. Surface cover was obtained from the digital Terra Class classification data (Almeida et al, 2016). This database contains 15 vegetation classes, such as forest, hydrology, urban areas, and several deforestation classes, including clean and dirty pasture, deforested areas, and exposed soil.…”
Section: Rainfall Sensitivity To Surface Covermentioning
confidence: 99%
“…In this work, underwater sediment cores were named "XC" and sediment cores from islands were named "EMB." Land-use map from Almeida et al (2016).…”
Section: Sediment Samplingmentioning
confidence: 99%
“…Sentinel 2A and 2B, 30 m spatial resolution; ESA, 2017), which will provide a good foundation to derive new land cover datasets in the coming years. Even existing Landsat and MODIS satellite archives are currently used to derive new land cover change datasets which can be implemented in our approach, for example the Terra Class datasets for the Brazilian Amazonas region (Almeida et al, 2016;Centro Regional da Amazônia, 2017).…”
Section: Evaluation Of Methods and Processingmentioning
confidence: 99%