Abstract
The Rio Negro basin is characterized by the extensive occurrence of podzol-type soils that store large amounts of organic matter in depth, resulting in the storage of carbon able available to the atmosphere with climate change. The quantification of this carbon requires determination of podzol types and their spatial distribution. Remote-sensing techniques would be helpful in indirect spatializing and segmentation of soil groups in Amazonian podzols. Here we associated remote-sensing images (Shuttle Radar Topographic Mission (SRTM), Operational Land Imager sensor/Landsat 8, and Thermal Infrared Sensor/Landsat 8) and field sample data in order to achieve carbon stock mapping. We found that a multi-sensor approach was critical for a proper segmentation of vegetation groups and spatial distribution of areas with different hydrologic soil regimes.
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