Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data Sources
2.3. Driving Factors Selection and Data Processing
3. Methodology
3.1. Estimation of Ecosystem Service Value
3.1.1. Calculation of ESV
3.1.2. Correction of Equivalent Factor
3.2. Spatial Autocorrelation
3.2.1. Univariate Spatial Autocorrelation
3.2.2. Bivariate Spatial Autocorrelation
3.3. Spatial Heterogeneity
3.3.1. GDM Factor Detection
3.3.2. GDM Interaction Detection
3.4. Research Framework
4. Results and Analysis
4.1. The Spatiotemporal Characteristics of ESV
4.2. Correlation Analysis Results of Spatial Factors
4.3. Moran Bivariate Analysis
4.4. GDM Factor Detection
4.4.1. Single Factor Detection
4.4.2. Two-Factor Interaction Detection
5. Discussion
5.1. The Reason for the Formation of the Spatial Distribution of ESV
5.2. Driving Mechanism of ESV
5.3. Policy Recommendations for the Liuxi River Basin
5.4. Study Limitations and Prospects for Future Research
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Category | Type of Data | Resolution | Time (Year) | Source |
---|---|---|---|---|
Land use/cover Data | Raster data | 30 m | 2005, 2010, 2015, 2018 | Land cover remote sensing monitoring data set for multi-period land use in China (CNLUCC). Available online: http://www.resdc.cn (assessed on 1 June 2022) |
Soil Erodibility | Raster data | 500 m | 2005, 2010, 2015 | Erosion Area of China in Five-year Increments. Available online: https://doi.org/10.3974/geodb.2021.05.03.V1 (assessed on 1 June 2022) |
Digital Elevation Model (DEM) | Raster data | 250 m | 2000 | NASA dataset. Available online: https://earthdata.nasa.gov (assessed on 1 June 2022) |
Average Monthly Precipitation | Raster data | 1000 m | 2005, 2010, 2015, 2018 | National Earth System Science Data Center. Available online: http://www.geodata.cn (assessed on 1 June 2022) |
Population | Raster data | 100 m | 2005, 2010, 2015, 2018 | WordPress Project. Available online: https://www.worldpop.org/ (assessed on 1 June 2022) |
Human Settlements (urban and rural) | Raster data | 30 m | 2005, 2010, 2015, 2017 | Impervious surface dataset. Available online: http://data.ess.tsinghua.edu.cn/ (assessed on 1 June 2022) |
16-day Net Primary Productivity (NPP) | Raster data | 500 m | 2005, 2010, 2015, 2018 | MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid. Available online: https://lpdaac.usgs.gov/products/mod17a3hgfv006/ (assessed on 1 June 2022) |
Water System | Vector data | / | 2017 | National Geographic Information Resources 1:1,000,000 National Basic Geographic Database. Available online: https://www.webmap.cn/ (assessed on 1 June 2022) |
Food Data | Statistical data | / | 2005, 2010, 2015, 2018 | Guangdong Statistical Yearbook. Available online: http://stats.gd.gov.cn/gdtjnj/ (assessed on 1 June 2022) |
Land Use Type | Dry Land | Paddy Field | Broadleaf Forest | Shrubs | Grass | Wetland | Water | Construction | Unused | |
---|---|---|---|---|---|---|---|---|---|---|
Ecosystem Classification | ||||||||||
Natural environment comprehensive service functions | food production | 0.85 | 1.36 | 0.29 | 0.19 | 0.38 | 0.51 | 0.8 | 0 | 0 |
raw materials | 0.4 | 0.09 | 0.66 | 0.43 | 0.56 | 0.5 | 0.23 | 0 | 0 | |
gas regulation | 0.67 | 1.11 | 2.17 | 1.41 | 1.97 | 1.9 | 0.77 | 0 | 0.02 | |
climate regulation | 0.36 | 0.57 | 6.5 | 4.23 | 5.21 | 3.6 | 2.29 | 0 | 0 | |
environmental purification | 0.1 | 0.17 | 1.93 | 1.28 | 1.72 | 3.6 | 5.55 | 0 | 0.1 | |
nutrient cycle maintenance | 0.12 | 0.19 | 0.2 | 0.13 | 0.18 | 0.18 | 0.07 | 0 | 0 | |
biodiversity maintenance | 0.13 | 0.21 | 2.41 | 1.67 | 2.18 | 7.87 | 2.55 | 0 | 0.02 | |
aesthetic landscape | 0.06 | 0.09 | 1.06 | 0.69 | 0.96 | 4.73 | 1.89 | 0 | 0.01 | |
Water source conditions comprehensive service functions | water supply | 0.02 | −2.63 | 0.34 | 0.22 | 0.31 | 2.59 | 8.29 | 0 | 0 |
water regulation | 0.27 | 2.72 | 4.74 | 3.35 | 3.82 | 24.23 | 102.24 | 0 | 0.03 | |
Soil conservation service function | soil conservation | 1.03 | 0.01 | 2.65 | 1.72 | 2.4 | 2.31 | 0.93 | 0 | 0.02 |
2005 | 2010 | 2015 | 2018 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
p | TOL | VIF | p | TOL | VIF | p | TOL | VIF | p | TOL | VIF | |
SLO | 0 | 0.824099 | 1.213447 | 0 | 0.777684 | 1.285870 | 0 | 0.755122 | 1.324289 | 0 | 0.749637 | 1.333978 |
GRE | 0 | 0.888490 | 1.125505 | 0 | 0.827045 | 1.209124 | 0 | 0.814747 | 1.227374 | 0 | 0.809450 | 1.235407 |
UR | 0 | 0.640032 | 1.562421 | 0 | 0.559448 | 1.787476 | 0 | 0.562794 | 1.776848 | 0 | 0.551702 | 1.812574 |
POP | 0 | 0.662943 | 1.508425 | 0 | 0.611686 | 1.634826 | 0 | 0.660070 | 1.514992 | 0 | 0.664477 | 1.504943 |
RDC | 0 | 0.845667 | 1.182499 | 0 | 0.838894 | 1.192046 | 0 | 0.827211 | 1.208882 | 0 | 0.823948 | 1.213669 |
SLO | RDC | GRE | UR | POP | |
---|---|---|---|---|---|
2005 | 0.760 | 0.993 | 0.923 | 0.666 | 0.857 |
2010 | 0.760 | 0.993 | 0.924 | 0.751 | 0.865 |
2015 | 0.760 | 0.993 | 0.929 | 0.805 | 0.858 |
2018 | 0.760 | 0.993 | 0.938 | 0.815 | 0.857 |
Dominant Interaction | 2005 | 2010 | 2015 | 2018 | ||||
---|---|---|---|---|---|---|---|---|
1 | SLO∩POP | 0.48914 | SLO∩RDC | 0.50597 | SLO∩RDC | 0.51013 | SLO∩RDC | 0.51112 |
2 | SLO∩RDC | 0.49076 | SLO∩POP | 0.49619 | SLO∩UR | 0.50460 | SLO∩UR | 0.50825 |
3 | SLO∩GRE | 0.48693 | SLO∩GRE | 0.49619 | RDC∩UR | 0.50105 | RDC∩UR | 0.50501 |
4 | SLO∩UR | 0.47996 | SLO∩UR | 0.49606 | SLO∩POP | 0.50009 | SLO∩GRE | 0.50279 |
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Ding, X.; Shu, Y.; Tang, X.; Ma, J. Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns. Land 2022, 11, 1852. https://doi.org/10.3390/land11101852
Ding X, Shu Y, Tang X, Ma J. Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns. Land. 2022; 11(10):1852. https://doi.org/10.3390/land11101852
Chicago/Turabian StyleDing, Xue, Yuqin Shu, Xianzhe Tang, and Jingwen Ma. 2022. "Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns" Land 11, no. 10: 1852. https://doi.org/10.3390/land11101852
APA StyleDing, X., Shu, Y., Tang, X., & Ma, J. (2022). Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns. Land, 11(10), 1852. https://doi.org/10.3390/land11101852