Applicability of the Mix–Unmix Classifier in percentage tree and soil cover mapping

Abstract

The Mix–Unmix Classifier is a simple novel method developed to address the problem of under‐determination in linear spectral unmixing. This paper tests the applicability of the Mix–Unmix Classifier in percentage mapping of tree cover and different soil types from single bands of satellite imagery. Various transformations were executed on African Moderate Resolution Imaging Spectroradiometer (MODIS) data bands 1, 2, 3, 4, 6 and 7. The equatorial rainforest is most distinguishable under skewness. The skewness transformation band is unmixed into two endmembers: tree (endmember of interest) and non‐tree (background). The resulting percentage tree cover map was compared with a University of Maryland percentage tree cover map of the continent, giving a correlation coefficient of 0.87. Fraction images of three soil types were generated from Japanese Earth Resources Satellite (JERS) synthetic aperture radar (SAR) L‐band data covering a section of Jordan. The soil types considered were hardpan topsoil, Qaa topsoil, and topsoil of herbaceous layer. The correlation coefficients of the Mix–Unmix Classifier‐derived fraction images versus reference fraction images for the three soil types were 0.89, 0.87 and 0.89, respectively.

Acknowledgements

The MODIS data were downloaded from the University of Maryland Landcover facility (http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp?productID=16).