DOI: 10.5061/dryad.ns1rn8q0p
title: Distribution pattern of rocky desertification in southwest China and analysis of its main driving factors based on GIS and Geodetector

description: Rocky desertification, a pressing environmental concern in Southwest
 China, significantly impacts local living conditions and regional
 sustainability. Employing remote sensing on a macro scale, this study
 focuses on identifying and analyzing the spatial distribution and driving
 factors of rocky desertification. Conducted in Southwest China, using
 Landsat data from Google Earth Engine for 2020, the research
 quantitatively extracts information on rocky desertification patches
 through traditional methods. Excluding unlikely areas using land use data,
 spatial distribution features and driving factors are examined via GIS
 spatial analysis and a geodetector model. The main conclusions are as
 follows. Rocky desertification covers 217,530.4 km2 (accounting for 15.6%
 of Southwest China), with areas of slight, moderate, and severe rocky
 desertification at 81.3%, 7.1%, and 11.6%, respectively. Spatially, rocky
 desertification primarily occurs in areas where lithology is carbonate
 rock between clastic rocks and continuous limestone, slope exceeds 15°,
 elevation ranges is 1000–2000 m, land use types are grassland and
 woodland, precipitation is 80–120 mm, and population density is below 50
 people/km2. Human activities have minimal influence. Geodetector analysis
 identifies lithology, land use type, and slope as primary driving factors,
 with interactive effects of lithology and land use type and slope and land
 use type jointly influencing rocky desertification formation in Southwest
 China.
description: The rocky desertification data were obtained from Landsat 8
 operational land imager (OLI) image data provided by the U.S. Geological
 Survey (USGS) ("https://www.usgs.gov"), de-clouded based on the
 Google Earth Engine (GEE), and atmospherically corrected using ENVI5.3
 Fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH)
 atmospheric correction tool with a spatial resolution of 30 m [37,38]. The
 land use type data with a spatial resolution of 1000 m were downloaded
 from the Resource and Environmental Science and Data Center of the Chinese
 Academy of Sciences ("https://www.resdc.cn"). The land use types
 in these data mainly include watersheds, rivers, and urban industrial
 construction land, cultivated land, woodland, grassland, and unutilized
 land. The overall accuracy of this dataset reached 95.41%, which met the
 needs of this study. The digital elevation model (DEM) data for the study
 area were obtained from the Geospatial Data Cloud Platform of the Computer
 Network Information Center of the Chinese Academy of Sciences
 ("http://www.gscloud.cn"), with a spatial resolution of 30 m.
 The precipitation data for 2020 were obtained from the China
 Meteorological Administration ("http://www.cma.gov.cn/"). The
 monthly average precipitation data of Southwest China for 2020 were
 obtained after kriging interpolation. The population density data were
 obtained from the 2020 Yearbook of the National and Local Government
 Statistical Bureau
 ("http://www.stats.gov.cn/tjsj/ndsj/").
description: Data Description: \[Access this dataset on Dryad\]\(DOI:
 10\.5061/dryad\.ns1rn8q0p\) ## Description of the data and file structure
 This dataset encompasses various influencing factors related to land
 degradation in Southwest China, alongside a collection of remote sensing
 data used for extracting the extent of land degradation. ### Influencing
 Factor Data 1. Elevation\_Southwest: * Data Type: Raster Data *
 Resolution: 30 meters * Unit: Meters * Description: Elevation data and
 Slope data for the Southwest China. 2. Lithology\_SouthwestChina: * Data
 Type: Vector Data (Shapefile) * Description: This is about the rock type
 data of the southwestern region of China, including five provinces:
 Sichuan, Yunnan, Guangxi, Guizhou, and Chongqing. It includes the
 distribution of carbonate rocks, limestone, dolomite, and other types. 3.
 People\_density\_SouthwestChina: * Data Type: Raster Data * Resolution: 1
 km * Unit: People/km² * Description: Population density data for Southwest
 China. 4. Precipitation2020\_China: * Data Type: Raster Data * Resolution:
 1km * Unit: Millimeters * Description: Precipitation data for China in the
 year 2020.Monthly average rainfall data for 12 months in total, e.g.
 "pre\_2020\_1" represents the average rainfall for January 2020
 5. Slope\_SouthwestChina: * Data Type: Raster Data * Resolution: 30 meters
 * Unit: Degrees * Description: Southwest\_Slope-Slope data for Southwest
 China. Slope\_Reclass:0–8°, 8–15°, 15–25°, 15–25°, four categories. ###
 Remote Sensing Data for Land Degradation Extraction 1.
 Southwest\_Albedo\_MODIS\_006\_MCD43A3: * Data Type: Raster Data *
 Resolution: 500 meters * Description: MODIS\_006\_MCD43A3 is one of the
 surface reflectance data products collected by the Moderate Resolution
 Imaging Spectroradiometer (MODIS) satellite sensor. MODIS is a
 collaboration between NASA (National Aeronautics and Space Administration)
 and NOAA (National Oceanic and Atmospheric Administration), designed to
 monitor various physical parameters of the Earth's surface,
 atmosphere, land, and oceans.This data range is for Southwest China.The
 time frame for this data is 2000 to 2020 2.
 Southwest\_LST\_MODIS\_006\_MOD11A2: * Data Type: Raster Data *
 Resolution: 1 km * Description: MODIS\_006\_MOD11A2 is one of the data
 products provided by the Moderate Resolution Imaging Spectroradiometer
 (MODIS) satellite sensor, offering information on land surface temperature
 and fire points.This data range is for Southwest China.The time frame for
 this data is 2000 to 2020 3. Southwest\_NDVI\_MODIS\_006\_MOD13A1: * Data
 Type: Raster Data * Resolution:250 meters *
 Description:MODIS\_006\_MOD13A1 is one of the vegetation index data
 products provided by the Moderate Resolution Imaging Spectroradiometer
 (MODIS) satellite sensor.This data range is for Southwest China.The time
 frame for this data is 2000 to 2020 4.
 Southwest\_TGSI\_MODIS\_006\_MCD43A4: * Data Type: Raster Data *
 Resolution: 500 meters * Description: MODIS\_006\_MCD43A4 is one of the
 data products provided by the Moderate Resolution Imaging
 Spectroradiometer (MODIS) satellite sensor, offering information on land
 surface bidirectional reflectance distribution function (BRDF) and
 albedo.TGSI is obtained by calculating.This data range is for Southwest
 China.The time frame for this data is 2000 to 2020 ## Sharing/Access
 information This document provides an overview of the data accessibility
 for the following five websites: 1. US Geological Survey (USGS)
 (https://www.usgs.gov): Data Types: Geological, geographical,
 hydrological, and various other fields. Open Access Policy: USGS offers
 extensive open data, allowing users to access, download, and use the data
 free of charge. 2. National Earth System Science Data Sharing Service
 Platform (http://www.gscloud.cn): Data Types: Diverse data covering the
 field of Earth system science. Open Access Policy: The platform is
 committed to data sharing, enabling users to freely access data according
 to the platform's regulations. 3. Resource and Environmental Science
 Data Center (https://www.resdc.cn): Data Types: Data related to resource
 and environmental science. Open Access Policy: The data center supports
 open access, and users can find detailed data download information on the
 website. 4. China Meteorological Administration (http://www.cma.gov.cn):
 Data Types: Meteorological data, including weather and climate-related
 information. Open Access Policy: The China Meteorological Administration
 provides some open data through its website, allowing users to access
 meteorological information. 5. National Bureau of Statistics of China -
 Annual Data (http://www.stats.gov.cn/tjsj/ndsj): Data Types: Annual
 statistical data covering various fields. Open Access Policy: The National
 Bureau of Statistics of China provides open accessto annual statistical
 data through its official website, allowing users to download and use the
 data. All users can use this dataset for free