DOI: 10.5061/dryad.f4qrfj74g
title: Study on the spatiotemporal changes and driving factors of habitat quality in the Yarlung Zangbo River from 2000 to 2020

description: The Yarlung Zangbo River (YLZB), the world’s highest plateau river,
 possesses a particularly fragile ecosystem, making it highly vulnerable to
 global climate change. Understanding changes in YLZB habitat quality and
 their driving mechanisms is crucial for ecological protection and
 sustainable development in the region. Based on land use data from 2000 to
 2020, we conducted a quantitative study on the spatiotemporal changes and
 driving mechanisms of habitat quality in the YLZB. This study employed
 habitat quality model, Land Use Transition Matrix , optimal parameter
 geographical detector and partial least squares structural equation model
 (PLS-SEM). The results show that: 1) Forests, grasslands, and unused land
 account for 94.14% of the basin area. The areas of unused land, forest
 land, and water bodies have continuously increased, while the areas of
 grasslands, permanent glaciers, and snowfields have continuously
 decreased. The decline was most pronounced from 2005 to 2010. 2) The
 habitat quality in the study area is higher in the southeast and lower in
 the west. The area of degraded habitats is significantly larger than that
 of improved habitats. 3) NDVI, elevation, and annual average temperature
 are key factors affecting changes in habitat quality. Elevation indirectly
 affects NDVI by influencing climate conditions, leading to a decline in
 habitat quality. This study provides  a scientific basis for
 understanding ecological trends in YLZB habitat quality, it provides new
 insights into the intrinsic driving mechanisms in high-altitude regions,
 and it offers theoretical support for relevant departments to implement
 sustainable management and conservation efforts.
description: Data sources include land use data for five periods (2000, 2005,
 2010, 2015, and 2020) in the study area, a digital elevation model (DEM),
 slope, natural environmental factors (mean annual precipitation, mean
 annual temperature,  normalized vegetation index (NDVI), and soil types),
 and socioeconomic data (population density, GDP, and nighttime lights) .
 Based on land use classification standards, the watershed land use types
 are divided into seven primary categories (cropland, forestland,
 grassland, water bodies, built-up land, unused land, and permanent glacier
 and snow cover) and 24 secondary categories . Slope data were extracted
 from DEM using the slope tool in ArcGIS 10.8. Combining previous research
 and practical conditions , a 9000×9000 grid was created using the fishnet
 tool in ArcGIS 10.8. The zonal statistics tool was used to obtain the mean
 values of various driving factors, serving as the smallest analysis unit
 and data carrier. To ensure spatial precision consistency across all data,
 the projection coordinate system was standardized to WGS_1984, with a
 spatial resolution of 30 m.
description: # Study on the Spatiotemporal Changes and Driving Factors of Habitat
 Quality in the Yarlung Zangbo River from 2000 to 2020
 [https://doi.org/10.5061/dryad.f4qrfj74g](https://doi.org/10.5061/dryad.f4qrfj74g) ## Description of the data and file structure This study obtained land use data for 2000, 2005, 2010, 2015, and 2020 from the Resource and Environment Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/). The data were clipped using the boundary of the Yarlung Tsangpo River for habitat quality calculation, with threat sources identified based on relevant literature and sensitivity tables assigned values. Driving factors include natural environmental factors (digital elevation model (DEM), slope, annual precipitation, annual temperature, normalized vegetation index, and soil type) and socioeconomic factors (population density, GDP, and nighttime lights). These were masked, unified to the WGS_1984 coordinate system, and resampled to 30m in ArcGIS. A fishnet was created using the fishnet tool in ArcGIS, resulting in 3,189 grids. The zonal statistics tool was used to obtain the mean values of each driving factor raster data within the grids, and an Excel spreadsheet was created for analysis using the OPGD package and PLS-SEM software in R Studio . ## Code/software setwd("D:/OPGD") install.packages("GD") library(GD) data&lt;-read.csv("3km.csv",as.is = TRUE) head(data)[1:5,] discmethod &lt;-c("equal","natural","quantile","geometric","sd") discitv &lt;-c(3:8) datagdm &lt;- gdm(Y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7+ X8+ X9+ X10, continuous_variable =c("X1", "X2","X3","X4","X5","X6","X8","X9","X10"), data = data, discmethod = discmethod, discitv= discitv) datagdm plot(datagdm)