Evaluation of ecosystem service capacity using the integrated ecosystem services index at optimal scale in Central Yunnan, China
Abstract
Understanding and quantifying the dynamic features of local ecosystem services (ESs) and integrating various ecosystem assessment results are crucial foundations for regional ES management. However, existing methods for integrating and objectively evaluating multiple ESs remain limited. Therefore, this research evaluates four key services based on the InVEST and RUSLE models in the Central Yunnan Province (CYP) —during 2000 to 2020: water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC). It then constructs an Integrated Ecosystem Service Index (IESI) using Principal Component Analysis (PCA). Additionally, this study explores the factors driving the spatial divergence of ESs by employing the optimal parameters-based geographical detector model (OPGD) at the optimal spatial scale. This study offers a more scientific and effective approach to evaluating regional integrated ecosystem service capacity. It provides a comprehensive analysis tool for weighing land use competition and evaluating the effect of policy. implementation.
https://doi.org/10.5061/dryad.6t1g1jx93
Description of the data and file structure
This study focuses on three main aspects. First, we selected WY, SC, CS, and HQ as key indicators related to human well-being to assess ESs using the InVEST and RUSLE models from 2000 to 2020 under the contexts of water scarcity, severe soil erosion, and habitat degradation in CYP. Second, we proposed an integrated method based on principal component analysis (PCA) to construct IESI. Finally, we applied the optimal parameters-based geographical detector (OPGD) model to identify the main driving factors in CYP.
Files and variables
This study obtained land use data, remote sensing data, meteorological data, and other relevant datasets for the years 2000, 2005, 2010, 2015, and 2020. The data were clipped using the boundary of Central Yunnan Province to calculate water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC). An integrated method based on Principal Component Analysis (PCA) was proposed to construct the Integrated Ecosystem Services Index (IESI). The driving factors (Table 1)included artificial nighttime light (X1,), land reclamation rate (X2), population density (X3), land use intensity (X4), slope (X5), relief degree of land surface (X6), elevation (X7), aspect (X8), temperature (X9), precipitation (X10), aridity index (X11), evaporation (X12), net primary productivity (X13), and the normalized difference vegetation index (X14). These datasets were masked, unified to the WGS_1984 coordinate system, and resampled to a 30m resolution in ArcGIS. A fishnet grid of 5000m was created using the fishnet tool in ArcGIS, and the zonal statistics tool was employed to obtain the mean values of each driving factor raster within the grids. An Excel spreadsheet was then created for analysis using the OPGD package in R Studio.
Table 1: Driver type selection and description
| Driving factors | Description | Unit |
|---|---|---|
| Artificial Nighttime-light（X1） | Characterizing the overall state of the regional economy | J/m^2 |
| Land reclamation rate（X2） | Reflecting the degree of land development | % |
| Population density（X3） | Characterizes the distribution of resident population | Person/km^2 |
| Land use intensity（X4） | Reflecting the extent of the impact of human activities on natural ecosystems | / |
| Slope（X5） | Characterize the degree of inclination of the ground surface at the point | ° |
| Relief degree of land surface（X6） | Reflecting changes in regional topographic relief | m |
| Elevation（X7） | Reflecting regional differences in topography | m |
| Aspect（X8） | Characterize the orientation of terrain slopes | ° |
| Temperature（X9） | Characterize regional hot and cold conditions | °C |
| Precipitation（X10） | It is the most fundamental part of the water cycle | mm |
| Aridity index（X11） | Characterize the degree of wetness or dryness of the area | % |
| Evaporation（X12） | Characterizing regional water demand | mm |
| Net primary productivity（X13） | Reflects the ability of vegetation to fix atmospheric CO2 | gC/㎡ |
| Normalized Difference Vegetation Index（X14） | Characterize the state of vegetation growth in the region | / |
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