Current Situation of Land Use in Three-gorges Reservoir Region in 2010

Land is rare natural resource.Production and construction of all sectors in a region must be based on land.Thus,overall research and analysis on current situation of land use can reveal scope,depth and reasonableness of land use,is helpful for analyzing internal mechanism land use change,and can reflect production scale,level and characteristics,and also can provide basis for optimization and adjustment of land use structure.Based on RS and GIS technologies,with the aid of TM image data of the Three-gorges Reservoir Region in 2010,the data of current situation of land use in Three-gorges Reservoir Region was obtained,and current land use situation was analyzed using geographimetrics and landscape ecology methods.Results show that since natural,social and economic conditions are different,land type diversity,combination type and location index of counties in the Three-gorges Reservoir Region are varied.In the land use diversity index,Xingshan County has the most single land use type(mainly forest land); in land use degree,Yuzhong District has the highest land use degree; in the integrated index of land use,Changshou District has the lowest integrated index of land use.This study is expected to provide reference and basis for formulating policies of protecting ecological environment of the Three-gorges Reservoir Region.

A comprehensive framework for assessing the spatial drivers of flood disasters using an Optimal Parameter-based Geographical Detector-machine learning coupled model

Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.