Study on the spatiotemporal changes and driving factors of habitat quality in the Yarlung Zangbo River from 2000 to 2020
Abstract
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.
README: 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
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<-read.csv("3km.csv",as.is = TRUE)
head(data)[1:5,]
discmethod <-c("equal","natural","quantile","geometric","sd")
discitv <-c(3:8)
datagdm <- 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)
Methods
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.