A novel‐optimal monitoring model of rocky desertification based on feature space models with typical surface parameters derived from LANDSAT_8 OLI

Abstract

AbstractPrevious studies monitoring the spatial distribution of rocky desertification have used single indices, a comprehensive index, or image classification. However, these approaches could not distinguish between the degrees of rocky desertification as they did not consider various influencing factors and their interactions. To avoid the above shortcomings, this study used the feature space model and seven typical land surface parameters to establish two categories of rocky desertification model: (1) a point‐to‐point model; and (2) a point‐to‐line model. A novel model for the optimal monitoring of rocky desertification was then proposed, which could take the comprehensive impacts of the human‐nature system on the process of rocky desertification. The results showed that: (1) the feature space models provided a novel approach to large‐scale monitoring of rocky desertification; (2) the point‐to‐line model incorporating the rock bare index (RBI)‐dryness index feature space showed the optimal applicability for monitoring of rocky desertification, with a precision of 93.4%; and (3) the RBI performed the best in indicating the process of rocky desertification, with an average precision of 88.5%. The results of this study can act as a reference within the investigation of the spatiotemporal evolution of rocky desertification for karst mountain areas.