A high‐accuracy vegetation restoration potential mapping model integrating similar habitat and machine learning

Abstract Vegetation restoration potential (VRP) mapping provides important information for ecosystem restoration planning. However, the inappropriate assumption of traditional models that VRPs are identical within an individual similar habitat unit may result in low accuracy of VRP maps. This study proposes an improved data‐driven model, namely, the similar habitat and machine learning‐based VRP mapping (SHMLVRPM) model. This new model introduces a variety of machine‐learning models to mine information on geographical environment heterogeneity in areas of similar habitat, which helps to improve the accuracy of VRP maps. Taking Yan'an City, Shanxi Province, China as our study area, we demonstrate the modelling process and validate the model. Our results show that the SHMLVRPM model can effectively construct high‐accuracy VRP maps, and its information entropy is approximately 5.8 greater than that derived from the traditional models. The random forest method has the highest prediction accuracy ( R 2 = 0.8) among the tested machine‐learning methods. The average VRP value of Yan'an is approximately 68%; counties with the low VRP achievement are concentrated in the northern part of Yan'an, only 54%. Our research results can assist policymakers in optimizing vegetation restoration options and promoting the protection and sustainable development of fragile ecosystems.