An iterative unmixing approach in support of fractional cover estimation in semi-arid environments

During the last decades, human activities endanger the biological and economic productivity of drylands, observable by processes like soil erosion and long-term loss of vegetation. To identify these changes and underlying driving processes it is essential to monitor the current state of the environment and to include this information in land degradation models. A frequently used input parameter is the degree of vegetation surface cover, thus there is a demand for quantitative cover estimation of large areas. Multispectral remote sensing has a limited ability to discriminate between dry vegetation components and bare soils. Therefore hyperspectral remote sensing is thought to be a possible source of information when applying adequate preprocessing and specific spectroscopic methodologies. The proposed approach is based on multiple endmember spectral unmixing, where the mixture model is iteratively improved using residual analysis and knowledge-based feature identification. It is believed that this automated methodology can provide quantitative fractional cover estimates for major ground cover classes as well as qualitative estimates of scene components. This apporach is currently tested using HyMap imaging spectrometer data of Cabo de Gata, Southern Spain, and will be adapted to larger areas based on hyperspectral data of future satellite instruments.