Total carbon mapping with hyperspectral unmixing techniques

Abstract

Depending on the ground sampling distance of a remote sensor, a pixel of a spectral data cube is represented as a combination of the reflected signals of the materials which constitutes the observed pixel. Hyperspectral unmixing algorithms model the pixel of a data cube to determine and extract the spectral signatures of its components, namely endmembers, with their corresponding abundance fractions. This study first reviews the interaction and mitigation mechanisms of heavy metals with carbon content in soil, specifically due to coal mining activities and thermal plants. Such mechanism is then investigated with hyperspectral unmixing techniques by producing total carbon maps for an abandoned coal mine site. The utilized data for the study area is obtained on August 2013 with multispectral Worldview-2 satellite sensor. The acquired image is orthorectified and atmospherically corrected for radiance to reflectance conversion prior to the analysis. The soil samples are mainly collected from the problematic regions in terms of soil pollution. The samples are analyzed with LECO TrueSpec CHN_S device to measure total carbon levels, which are employed as ground truth to assess the performance of unmixing algorithms. The resulting abundance maps for carbon content are found to have a high compatibility with each other and the ground truth data, which effectively point out the regions of high carbon content.