Use of remote sense imagery for mapping deep plant-derived carbon storage in Amazonian podzols in regional scale

The upper region of Rio Negro basin is characterized by the occurrence of extensive Podzol zones. These soils have a great capacity of stocking high contents of carbon, thus it is important to understand the spatial distribution of Podzols to quantify the carbon content stored on the Amazon biome. Due the lack of studies developed on this region, remote sensing techniques would be helpful on the indirect spatialization and quantification of total carbon stored on Amazon Podzols. Therefore this study aimed to propose association methods between remote sensing images and field sample data, for mapping carbon stocks on Amazon Podzols. As a result it was observed that a multisensor approach was crucial for the proper segmentation of vegetation groups and spatial distribution of areas with different soil water regimes, based on filed sample data and remote sensing images (sensors: PALSAR/ALOS; ETM/Landsat 7).