Interactions and Driving Force of Land Cover and Ecosystem Service Before and After the Earthquake in Wenchuan County
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Overview
2.2. Data and Preprocessing
2.3. Methods
2.3.1. Ecological Indicators Assessment
2.3.2. Trade-Off/Synergy Analysis of Ecological Indicators
Correlation Analysis
Geographically Weighted Regression (GWR)
2.3.3. Identification of Ecological Indicator Bundles
2.3.4. Driving Force Analysis
3. Results
3.1. Spatiotemporal Distribution Characteristics
3.1.1. Spatial Distribution Characteristics
3.1.2. Temporal Variation Characteristics
3.2. Trade-Offs and Synergies Between Ecological Indicators
3.2.1. Results of Correlation Analysis
3.2.2. Results of GWR
3.3. Identification of Ecological Indicator Bundles
3.4. Results of Driving Force Analysis
4. Discussion
4.1. Spatiotemporal Evolution of LC and ESs
4.2. Interactions Between LC and ESs
4.3. Analysis of Driving Mechanisms
4.4. Significance of the Study and Policy Recommendations
4.5. Limitations and Prospects
5. Conclusions
- (1)
- The spatial distribution of ecological indicators exhibited a significant “gradient effect”: FVC, CS, and SC decreased with increasing elevation, showing a “high in the east, low in the west” distribution pattern; FR, HQ, and WY increased with increasing elevation, showing a “high in the west, low in the east” distribution pattern.
- (2)
- The earthquake had a significant but periodic impact on LC. Post-earthquake, FVC showed a “V”-shaped fluctuation, while FR showed an inverted “V”-shaped fluctuation. However, in most areas, vegetation and rock exposure had recovered to pre-earthquake levels by 2015, indicating a clear “recovery period”. In contrast, ESs were less affected by the earthquake. HQ and CS decreased slightly after the earthquake, while SC and WY showed an upward trend driven by climate change from 2000 to 2020.
- (3)
- Ecological indicators could be divided into two groups based on trade-off and synergy relationships. One group consists of FVC, CS, and SC, while the other group consists of FR, HQ, and WY. Ecological indicators within the same group exhibited synergy, while those between different groups exhibited trade-offs. The trade-off and synergy relationships of ecological indicators were similar across different scales but were more pronounced at larger scales and showed strong spatial heterogeneity.
- (4)
- Ecological indicators were driven by both natural and human factors, with elevation being a key driving factor. Elevation (explainability > 45%) was the primary driver of FVC and FR, and the earthquake temporarily enhanced the driving effect of lithology. The spatial distribution and variation of ESs are driven by multiple factors, including topography, climate, and human activities, all of which are closely related to the elevation gradient.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Type | Formula | |
---|---|---|
Coverage Indicators | NDVI | |
Where and are the red band and the near-infrared band of remote sensing images (Landsat TM/OLI), respectively. | ||
NDRI | ||
where and are the short-wave infrared band-1 and the near-infrared band of remote sensing images (Landsat TM/OLI), respectively. | ||
Ecosystem Service | Habitat quality | |
Here, Qxj represents the habitat quality of a grid within habitat type j, where Dxj indicates the disturbance level experienced by grid in habitat type . The constant is the half-saturation value, typically set at half of the maximum value obtained from during a preliminary trial, and , denotes the suitability of habitat type . | ||
Carbon storage | ||
In this equation, denotes above-ground organic matter, denotes below-ground organic matter, represents dead organic matter, and represents soil organic matter. | ||
Ecosystem Service | Soil conservation | |
In this equation, represents soil conservation capacity, while , denote potential and actual soil erosion, respectively. , , , , and represent rainfall erosivity, soil erodibility, slope length, vegetation cover management, and soil conservation practices. | ||
Water yield | ||
In this equation, represents the water yield for pixel ; : the annual precipitation for pixel ; and represents the annual evapotranspiration for pixel . | ||
Spearman’s correlation analysis | ||
where is the correlation coefficient between and ; and are the sample values of and , respectively; and is the number of samples. | ||
Geographically Weighted Regression (GWR) | ||
where is the sample values at position , is the remained sample values at position ; are the spatial coordinates of sampling point ; is the intercept term; is the regression coefficient and is the error term. | ||
Extreme Gradient Boosting model (XGBoost model) | ||
where represents the loss function, indicates the regularization term that suppresses the overfitting of the base learner at each iteration, and it is not involved in the integration of the final model. corresponds to the first-order derivative of the loss function, refers to the second-order derderivative of the loss function, and denotes the iteration number. | ||
Shapley Additive Explanations (SHAP) | ||
where is the SHAP value for a specific feature , indicating its influence on the model’s prediction. This value is computed by evaluating the contributions of feature across all potential subsets of the other features (excluding ). |
Appendix B. XGBoost-SHAP Model Evaluation Metrics
2000 | 2005 | 2010 | 2015 | 2020 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | |
FVC | 0.96 | 0.04 | 0.05 | 0.96 | 0.03 | 0.05 | 0.92 | 0.05 | 0.06 | 0.94 | 0.04 | 0.06 | 0.94 | 0.04 | 0.06 |
FR | 0.94 | 0.05 | 0.07 | 0.94 | 0.04 | 0.06 | 0.89 | 0.05 | 0.08 | 0.91 | 0.05 | 0.07 | 0.91 | 0.05 | 0.07 |
HQ | 0.69 | 0.04 | 0.05 | 0.69 | 0.04 | 0.05 | 0.78 | 0.04 | 0.05 | 0.78 | 0.04 | 0.05 | 0.78 | 0.03 | 0.05 |
CS | 0.82 | 0.03 | 0.05 | 0.82 | 0.03 | 0.05 | 0.81 | 0.04 | 0.06 | 0.80 | 0.04 | 0.06 | 0.81 | 0.04 | 0.06 |
SC | 0.89 | 0.03 | 0.04 | 0.89 | 0.03 | 0.04 | 0.88 | 0.03 | 0.04 | 0.90 | 0.02 | 0.03 | 0.89 | 0.03 | 0.04 |
WY | 0.96 | 0.04 | 0.05 | 0.96 | 0.04 | 0.05 | 0.95 | 0.04 | 0.05 | 0.95 | 0.04 | 0.05 | 0.96 | 0.04 | 0.05 |
2000 | 2005 | 2010 | 2015 | 2020 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | |
FVC | 0.8 | 0.14 | 0.16 | 0.7 | 0.14 | 0.18 | × | 0.16 | 0.21 | 0.69 | 0.14 | 0.18 | 0.76 | 0.13 | 0.17 |
FR | 0.77 | 0.18 | 0.19 | 0.7 | 0.15 | 0.19 | 0.52 | 0.18 | 0.21 | 0.65 | 0.14 | 0.18 | 0.61 | 0.17 | 0.20 |
HQ | × | 0.13 | 0.19 | × | 0.15 | 0.21 | × | 0.14 | 0.19 | × | 0.15 | 0.19 | × | 0.14 | 0.18 |
CS | × | 0.25 | 0.28 | × | 0.26 | 0.3 | × | 0.28 | 0.34 | × | 0.28 | 0.35 | × | 0.26 | 0.33 |
SC | × | 0.15 | 0.27 | × | 0.15 | 0.26 | × | 0.15 | 0.27 | × | 0.16 | 0.28 | × | 0.15 | 0.28 |
WY | 0.85 | 0.11 | 0.14 | 0.82 | 0.12 | 0.15 | 0.83 | 0.13 | 0.14 | 0.81 | 0.13 | 0.15 | 0.84 | 0.12 | 0.13 |
Full Form | Abbreviation | Full Form | Abbreviation |
---|---|---|---|
Land cover | LC | Ecosystem service | ES |
Fractional vegetation coverage | FVC | Rock exposure rate | FR |
Habitat quality | HQ | Carbon storage | CS |
Soil conservation | SC | Water yield | WY |
Normalized difference vegetation index | NDVI | Normalized difference rock index | NDRI |
Geographically weighted regression | GWR | Self-organizing map | SOM |
Extreme gradient boosting | XGBoost | Shapley additive explanations | SHAP |
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Data type | Format | Resolution | Data Source |
---|---|---|---|
Administrative boundaries | Shpfile | / | National Platform for Common Geospatial Information Services (www.tianditu.gov.cn) |
Major road data | Shpfile | / | Open Street Map (www.openstreetmap.org) |
Land use data | Raster | 30 m | Resource and Environment Science and Data Center(www.resdc.cn) |
DEM | Raster | 30 m | Geospatial Data Cloud Platform (www.gscloud.cn) |
Precipitation | Raster | 1000 m | National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn) |
Evapotranspiration | Raster | 1000 m | National Tibetan Plateau Science Data Center (https://data.tpdc.ac.cn) |
Soli type | Raster | 250 m | Harmonized Worldwide Soil Data base: (HWSD) (http://www.fao.org) |
Geological data | Shpfile | / | Geoscientific Data and Discovery Publishing System (http://dcc.ngac.org.cn) |
FVC | FR | HQ | CS | SC | WY | |
---|---|---|---|---|---|---|
R2 | 0.95 | 0.92 | 0.75 | 0.81 | 0.89 | 0.96 |
MAE | 0.04 | 0.05 | 0.04 | 0.04 | 0.03 | 0.04 |
RMSE | 0.06 | 0.07 | 0.05 | 0.06 | 0.04 | 0.05 |
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Pang, J.; He, L.; He, Z.; Zeng, W.; Yuan, Y.; Bai, W.; Zhao, J. Interactions and Driving Force of Land Cover and Ecosystem Service Before and After the Earthquake in Wenchuan County. Sustainability 2025, 17, 3094. https://doi.org/10.3390/su17073094
Pang J, He L, He Z, Zeng W, Yuan Y, Bai W, Zhao J. Interactions and Driving Force of Land Cover and Ecosystem Service Before and After the Earthquake in Wenchuan County. Sustainability. 2025; 17(7):3094. https://doi.org/10.3390/su17073094
Chicago/Turabian StylePang, Jintai, Li He, Zhengwei He, Wanting Zeng, Yan Yuan, Wenqian Bai, and Jiahua Zhao. 2025. "Interactions and Driving Force of Land Cover and Ecosystem Service Before and After the Earthquake in Wenchuan County" Sustainability 17, no. 7: 3094. https://doi.org/10.3390/su17073094
APA StylePang, J., He, L., He, Z., Zeng, W., Yuan, Y., Bai, W., & Zhao, J. (2025). Interactions and Driving Force of Land Cover and Ecosystem Service Before and After the Earthquake in Wenchuan County. Sustainability, 17(7), 3094. https://doi.org/10.3390/su17073094