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.

Surface urban heat island effect and its spatiotemporal dynamics in metropolitan area: a case study in the Zhengzhou metropolitan area, China

“…The continuous heat storage by artificial surfaces in urban areas causes Frontiers in Environmental Science frontiersin.org higher daytime surface temperatures that result in stronger daytime SUHIIs in summer. In contrast, in suburban areas, where there are more agricultural activities or forest vegetation, the evapotranspiration effect of plants reduces surface temperatures (Yao et al, 2019;Meng et al, 2023). In this study, the lowest SUHIIs were detected in winter, indicating the presence of the cold island effect, which is associated with weak solar radiation and differences in the thermal properties of urban and suburban areas.…”

GWmodelS: A software for geographically weighted models

“…The early versions of GWmodel have acquired more than 400 citations (according to records in google scholar) as a preferred GW toolkit in a wide range of studies, e.g. meteorology [53,54], urban studies [55,56], and environmental science [57][58][59][60][61]. To promote better and broader usages of GW models, we developed this userfriendly, high-performance integrated and bigeminal software.…”

GWmodelS: A Software for Geographically Weighted Models

“…The early versions of GWmodel have acquired more than 400 citations (according to records in google scholar) as a preferred GW toolkit in a wide range of studies, e.g. meteorology [53,54], urban studies [55,56], and environmental science [57][58][59][60][61]. To promote better and broader usages of GW models, we developed this userfriendly, high-performance integrated and bigeminal software.…”