Land use and ecosystem service value spatiotemporal dynamics, topographic gradient effect and their driving factors in typical alpine ecosystems of the east Qinghai-Tibet Plateau: Implications for conservation and development
Data files
Jan 17, 2025 version files 10.02 KB
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README.md
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Supplemental_information_1.csv
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Supplemental_information_2.csv
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Supplemental_information_3.csv
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Abstract
Shiqu County, China, a representative of alpine ecosystems on the eastern Qinghai-Tibet Plateau, plays a crucial role in ecological functions. However, amid the backdrop of climate change and rapid land use transformation, its delicate alpine ecosystems remain persistently threatened, including several nature reserves and a Ramsar site within it. Consequently, this paper presents a comprehensive analysis of the temporal and spatial evolution, as well as the spatial differentiation characteristics, of the Ecosystem Service Value (ESV) in Shiqu County from 1980 to 2020, including an examination of its driving factors. The research findings reveal that: (1) From 1980 to 2020, Shiqu County experienced a general trend of diminishing unused land, grassland, and cultivated land, alongside an expansion of forested land, water areas, and construction land. (2) Throughout the study duration, the ESV of Shiqu County exhibited a growth trajectory, escalating from 26.180 billion CNY in 1980 to 26.848 billion CNY in 2020, representing a net increase of 2.55%. Grasslands played the most substantial role in contributing to Shiqu's ESV. (3) From 1980 to 2020, all ecosystem service functions exhibited ESV growth, with hydrological regulation and water supply notably increasing by 9.07% and 8.81%, respectively. (4) With increasing altitude, slope, aspect, and terrain ruggedness, the land use distribution exhibits significant vertical zonation, resulting in pronounced vertical variations in ESV, per unit area ESV, and ESV across various ecosystem service functions. (5) The spatial differentiation of ESV in Shiqu County is influenced by both natural and economic factors, with natural factors exerting a more substantial influence on spatial disparities than economic factors. The research provides in-depth insights for assessing ecosystem service values in similar regions. findings hold significance for guiding land management decisions and policy formulation on the Qinghai-Tibet Plateau, further advancing the overarching goals of sustainable development and ecological protection.
README: Land use and ecosystem service value spatiotemporal dynamics, topographic gradient effect and their driving factors in typical alpine ecosystems of the east Qinghai-Tibet Plateau: Implications for conservation and development
https://doi.org/10.5061/dryad.0cfxpnw93
Description of the data and file structure
We provided detailed data on the spatiotemporal dynamics of land use and ecosystem service value, the effects of topographic gradients, and their driving factors in typical alpine ecosystems of the Eastern Qinghai-Tibet Plateau.
Files and variables
File: Supplemental_information_1.csv
Description:
Variables
- Cultivated Land: Refers to areas used for agricultural activities, including the cultivation of crops such as grains, vegetables, and economic plants. Unit: km².
- Forest Land: Areas covered by forests, including both natural and artificial forests. Unit: km².
- Grassland: Natural areas dominated by grass vegetation, primarily used for livestock grazing. Unit: km².
- Water Area: Includes rivers, lakes, reservoirs, and wetlands. Unit: km².
- Construction Land: Areas used for buildings, industrial activities, transportation infrastructure, and urban development. Unit: km².
- Unused Land: Land currently not in use, such as deserts, bare land, or saline-alkali soil. Unit: km².
File: Supplemental_information_2.csv
Description:
Variables
- X1: Agricultural production potential (kg/km²)
- X2: NDVI (Normalized Difference Vegetation Index) (unitless, ranging from -1 to 1)
- X3: Average annual wind speed (m/s)
- X4: Distance from residential areas (km)
- X5: Average annual temperature (°C)
- X6: Average annual ground temperature (°C)
- X7: Annual sunshine (hours/year)
- X8: Average GDP (CNY/km²)
- X9: Distance from road (km)
- X10: Annual evaporation (mm/year)
- X11: Distance from rivers (km)
- X12: Average annual relative humidity (%)
- X13: Distance from reserves (km)
- X14: Average annual precipitation (mm/year)
- X15: ≥10°C accumulated temperature (°C·days/year)
- X16: Population density (persons/km²)
- X17: Slope (°)
- X18: RDLS (Radiation Density of Land Surface) (W/m²)
- X19: Altitude (m above sea level)
- X20: Aspect (° from north)
- X21: Land use types (categorical variable: e.g., forest, grassland, cultivated land, etc.)
File: Supplemental_information_3.csv
Description:
Variables
- X1: Agricultural production potential (kg/km²)
- X2: NDVI (Normalized Difference Vegetation Index) (unitless, ranging from -1 to 1)
- X3: Average annual wind speed (m/s)
- X4: Distance from residential areas (km)
- X5: Average annual temperature (°C)
- X6: Average annual ground temperature (°C)
- X7: Annual sunshine (hours/year)
- X8: Average GDP (CNY/km²)
- X9: Distance from road (km)
- X10: Annual evaporation (mm/year)
- X11: Distance from rivers (km)
- X12: Average annual relative humidity (%)
- X13: Distance from reserves (km)
- X14: Average annual precipitation (mm/year)
- X15: ≥10°C accumulated temperature (°C·days/year)
- X16: Population density (persons/km²)
- X17: Slope (°)
- X18: RDLS (Radiation Density of Land Surface) (W/m²)
- X19: Altitude (m above sea level)
- X20: Aspect (° from north)
- X21: Land use types (categorical variable: e.g., forest, grassland, cultivated land, etc.)
Code/software
Excel/ArcGIS10.8/R4.3.2
Access information
Other publicly accessible locations of the data:
- The study utilized land use grid data for the years 1980, 1990, 2000, 2010, and 2018, sourced from the Chinese Academy of Sciences Resource and Environment Science and Data Center (http://www.resdc.cn/). This dataset, derived from the interpretation of Landsat remote sensing imagery, has a resolution of 30m with an overall accuracy exceeding 90% (Liu et al., 2014). Land use types were classified into six categories according to the national land use remote sensing monitoring classification system: cultivated land, forest land, grassland, water areas, construction land, and unused land. Data on annual average wind speed, annual sunshine hours, annual average relative humidity, annual average ground temperature, annual evaporation, annual average precipitation, annual average temperature, NDVI, accumulated temperature ≥10℃, per capita GDP, and population density were also obtained from the Chinese Academy of Sciences Resource and Environment Science Data Center (http://www.resdc.cn/), with a resolution of 1km. Slope, aspect, and terrain ruggedness, with a resolution of 30m, were derived from DEM data sourced from the Geospatial Data Cloud (http://www.gscloud.cn/). Road, settlement, river, lake, and reservoir data were obtained from the National Basic Geographic Information Center (https://www.webmap.cn/).
Data was derived from the following sources:
- Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., Yan, C., Wu, S., Shi, X., Nan, J., Yu, D., Pan, X., Chi, W., 2014. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s. Acta Geographica Sinica 69, 12. https://doi.org/10.11821/dlxb201401001
- Xie, G., Zhang, C., Zhang, L., Chen, W., Li, S., 2015. Improvement of the Evaluation Method for Ecosystem Service Value Based on Per Unit Area. Journal of Natural Resources 30, 1243–1254.
Methods
We analyzed the spatiotemporal dynamics and spatial variation of ESV in the region from 1980 to 2020, employing methods such as the ESV evaluation model, terrain gradient classification, and geographic detector analysis. In addition, we explore the drivers behind ESV. Through a comprehensive analysis of the ecosystem service value in Shiqu County, the results of this study provide in-depth insights for evaluating the ecosystem service value in similar areas.
The study utilized land use grid data for the years 1980, 1990, 2000, 2010, and 2018, sourced from the Chinese Academy of Sciences Resource and Environment Science and Data Center (http://www.resdc.cn/). This dataset, derived from the interpretation of Landsat remote sensing imagery, has a resolution of 30m with an overall accuracy exceeding 90% (Liu et al., 2014). Land use types were classified into six categories according to the national land use remote sensing monitoring classification system: cultivated land, forest land, grassland, water areas, construction land, and unused land. Data on annual average wind speed, annual sunshine hours, annual average relative humidity, annual average ground temperature, annual evaporation, annual average precipitation, annual average temperature, NDVI, accumulated temperature ≥10℃, per capita GDP, and population density were also obtained from the Chinese Academy of Sciences Resource and Environment Science Data Center (http://www.resdc.cn/), with a resolution of 1km. Slope, aspect, and terrain ruggedness, with a resolution of 30m, were derived from DEM data sourced from the Geospatial Data Cloud (http://www.gscloud.cn/). Road, settlement, river, lake, and reservoir data were obtained from the National Basic Geographic Information Center (https://www.webmap.cn/).
Ecosystem service value assessment model
Based on the ESV (Ecosystem Service Value) assessment model (Costanza et al., 1997) (Formula 1), and referring to the unit area ESV equivalent table for terrestrial ecosystems in China (Chen and Zhang, 2000), the study calculates the ESV for Shiqu County. This calculation is based on the average grain yield of Shiqu County (2701.51 kg/ha) and the 2020 procurement prices for major crops in Sichuan Province (2.41 CNY/kg). Following the principle that "the unit area ESV is equal to one-seventh of the market economic value of the average grain yield per unit area" (Xie et al., 2015), the ESV value equivalent for Shiqu County is determined to be 930.09 CNY/ha. Subsequently, the ESV of the study area is calculated using the equivalent table with the following formula:
ESV = ∑∑ A_i × S_ij (1)
Sensitivity analysis
The sensitivity index can be used to verify the extent to which changes in ESV over time depend on fixed value coefficients (VC), thus reducing uncertainty in the results (Wang et al., 2022). Relevant scholars typically calculate the sensitivity index by increasing or decreasing the ecosystem service value coefficients for each land use type by 50% (Guo et al., 2022). The calculation formula is as follows:
CS = |(ESV_j - ESV_i) / ESV_i|/ ( (VC_jk - VC_ik) / VC_ik ) (2)
Terrain factors extraction
Select four terrain elements: altitude, slope, aspect, and terrain undulation to analyze the terrain gradient distribution of land use and ESV in Shiqu County. The elevation and slope are classified using the natural breakpoint method (Jin et al., 2023), and the aspect is classified based on previous research results (Fu et al., 2021). The terrain undulation is determined by the elevation difference between the highest and lowest points in a specific area (Zhang and You, 2013).
Driver factors analysis
Geodetector analysis is a statistical method used to detect and quantify the influence of explanatory variables on spatially stratified heterogeneity. It evaluates the extent to which a particular factor explains the spatial distribution of a dependent variable, offering insights into the driving forces behind observed patterns. Geodetector can explore the spatial differentiation characteristics of elements (Wang et al., 2010) and use factor detectors and interaction detectors to detect the driving factors and their interactions with ESV spatial differentiation in Shiqu County. The formula is as follows:
q = 1 - (1 / (Nσ²)) ∑ (N_h σ_h²) (3)
Standard deviation elliptic theory
The standard deviational ellipse method is a spatial analysis technique that reflects the spatial distribution characteristics and spatiotemporal evolution of the study object by describing fundamental parameters such as centroid, azimuth, major axis, and minor axis (Zhao et al., 2022). This method is used in this study to analyze the spatiotemporal distribution and evolution characteristics of ESV. The formulas for calculating the key parameters are as follows (Du et al., 2019).
X̄ = (∑ w_i x_i) / ∑ w_i (4)
Ȳ = (∑ w_i y_i) / ∑ w_i (5)
tan(α) = √[(∑ w_i x_i² - ∑ w_i x_i²)² + (∑ w_i y_i² - ∑ w_i y_i²)²] / [2 ∑ w_i x_i y_i] (6)
σ_x = √[∑ w_i (x_i cos(α) - w_i y_i sin(α))²] / ∑ w_i² (7)
σ_y = √[∑ w_i (x_i sin(α) - w_i y_i cos(α))²] / ∑ w_i² (8)
S = π σ_x σ_y (9)