DOI: 10.5061/dryad.0cfxpnw93
title: 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

description: 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.
description: 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/).
 <strong>Ecosystem service value assessment
 model</strong> 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） <strong>Sensitivity
 analysis</strong> 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） <strong>Terrain factors
 extraction</strong> 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). <strong>Driver factors
 analysis</strong> 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） <strong>Standard deviation elliptic
 theory</strong> 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）<br>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）<br>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）
description: # 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](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: Table S1.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: Table S2.csv **Description:**  ##### Variables * FP: Food production(10^8 CNY); * RMP: Raw material production(10^8 CNY); * WRS: Water resource supply(10^8 CNY); * GR: Gas regulation(10^8 CNY); * CR: Climate regulation(10^8 CNY); * EP: Environment purification(10^8 CNY); * WR: Water regulation(10^8 CNY); * SC: Soil conservation(10^8 CNY); * NCM: Nutrient cycle maintenance(10^8 CNY); * BM: Biodiversity maintenance(10^8 CNY); * AL: Aesthetic landscape(10^8 CNY); #### File: Table S3.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: Table S4.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: Original data 1.csv **Description:**  ##### Variables * CL: Cultivated land (km²) * FL: Forest land (km²) * GL :Grassland (km²) * WA :Water area (km²) * COL: Construction land (km²) * UL: Unused land (km²) #### File: Original data 2.csv **Description:**  ##### Variables * CL: Cultivated land (km²) * FL: Forest land (km²) * GL: Grassland (km²) * WA: Water area (km²) * COL: Construction land (km²) * UL: Unused land (km²) #### File: Original data 3.csv **Description:**  ##### Variables * CL: Cultivated land (km²) * FL: Forest land (km²) * GL: Grassland (km²) * WA: Water area (km²) * COL: Construction land (km²) * UL: Unused land (km²) #### File: Original data 4.csv **Description:**  ##### Variables * FP: Food production(10^8 CNY); * RMP: Raw material production(10^8 CNY); * WRS: Water resource supply(10^8 CNY); * GR: Gas regulation(10^8 CNY); * CR: Climate regulation(10^8 CNY); * EP: Environment purification(10^8 CNY); * WR: Water regulation(10^8 CNY); * SC: Soil conservation(10^8 CNY); * NCM: Nutrient cycle maintenance(10^8 CNY); * BM: Biodiversity maintenance(10^8 CNY); * AL: Aesthetic landscape(10^8 CNY); #### File: Original data5.csv **Description:**  ##### Variables * Extremely Low: Ecosystem Service Value** **grade * Low：Ecosystem Service Value** **grade * Medium：Ecosystem Service Value** **grade * High：Ecosystem Service Value** **grade * Extremely high：Ecosystem Service Value** **grade ## 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/](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/](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/](http://www.gscloud.cn/)). Road, settlement, river, lake, and reservoir data were obtained from the National Basic Geographic Information Center ([https://www.webmap.cn/](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](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. ## Version changes #### 18 Feb 2025 * Changed the names of tables to Table s1 - s4 to make them more in line with the links in the article. Table s4 is a newly added raw data table about ecosystem services under topographic gradients. * Added Original data tables 1 - 5. * Original data 1: land use change data from 1980 - 2020, * Original data 2: ecosystem service value data from 1980 - 2020, * Original data  3: land use data from 1980 - 2020, * Original data  4: ecosystem service data under different land uses, * Original data  5: ecosystem service data under different topographic gradients respectively.