An Optimal Monitoring Model of Desertification in Naiman Banner Based on Feature Space Utilizing Landsat8 Oli Image

Current feature space models of desertification were almost linear, which ignored the complicated and non-linear relationships among variables for monitoring desertification. Fully considering the influencing factors of the desertification process in Naiman Banner, four sensitive indices including MSAVI, NDVI, TGSI, and Albedo have been selected to construct five feature spaces. Then, the precisions of different feature space models for monitoring desertification information (including non-linear and linear models) have been compared and analyzed. The non-linear Albedo-MSAVI feature space model for Naiman Banner has higher efficiency with the overall precision of 90.1%, while that of Albedo-TGSI had the worst precision with 0.69. Overall, the feature space model (non-linear) of Albedo-MSAVI has the highest applicability for monitoring the desertification information in Naiman Banner.