Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Processing
2.3. Methods
2.3.1. ESs Evaluation and Validation
2.3.2. Spatial-Temporal Change Trend Analysis
2.3.3. ESs Hotspots Identification
2.3.4. Investigating the Complex Relationships among ESs
2.3.5. ESs Driving Mechanisms
3. Results
3.1. Spatial Patterns of ESs
3.2. Spatial Heterogeneity of ES Hotspots
3.3. Spatial-Temporal Trade-Offs and Synergies between ES Pairs
3.4. ES Bundles among Multiple ESs
4. Discussion
4.1. Exploring the Driving Mechanisms of ESs
4.2. Scale Effect of ESs and Their Complex Interactions
4.3. Implications for ESs Management and Urban Planning
4.4. Applications of Remote Sensing for ESs Evaluation
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Datasets | Data Sources | Resolution | Data Processing |
---|---|---|---|
Land use types | Geospatial Data Cloud Platform [37] | 30 m | Based on Landsat 7/ETM (2000), Landsat 5/TM (2010) and Landsat 8/OLI (2018) images from May to September during the vigorous growing season of vegetation with low cloud cover, the land use data were interpreted by the object-oriented classification method. The overall accuracy (OA) obtained from the confusion matrix based on the sample points from Google Earth was used in the accuracy assessment, with OA were 90.40%, 90.46% and 93.77%, respectively. |
Geomorphological dataset | Geospatial Data Cloud Platform [37] | 30 m | The ASTER GDEM V2 digital elevation model (DEM) data were obtained for slope, aspect, relief, terrain ruggedness index (TRI), topographic position index (TPI), terrain niche index (TNI) and sub-watershed extractions by SimDTA and ArcGIS. |
Meteorological dataset | National Meteorological Information Center [38] | 30 m | Temperature, precipitation and sunshine duration data were interpolated by ANUSPLIN 4.3 with data from 28 meteorological stations in the study area and its surrounding zones. |
Soil dataset | Harmonized World Soil Database v1.2 [39] | 1 km | The reference soil depth, salinity, sand, silt, clay, gravel and organic carbon content were resampled to 30 m resolution by cubic convolution interpolation. The soil saturated hydraulic conductivity was calculated based on the Soil Water Characteristics module in Soil Plant Atmosphere Water (SPAW). |
Vegetation dataset | MODIS13 and MODIS17 [40] | 250 m | The normalized difference vegetation index (NDVI) and net primary productivity (NPP) were resampled to 30 m resolution by cubic convolution interpolation. |
Distance factors | Land use data | 30 m | The distance to cultivated land, forestland, grassland, waters and construction land were calculated using Euclidean distance. |
Socioeconomic dataset | Resource and Environmental Science Data Center of the Chinese Academy of Sciences [41] | 1 km | The gross regional domestic product (GDP) was resampled to 30 m resolution by cubic convolution interpolation. |
WorldPop Dataset [42] | 500 m | The population (POP) was resampled to 30 m resolution by cubic convolution interpolation. | |
NPP-VIIRS-like NTL Data [43] | 500 m | The nighttime light data (NTL) was resampled to 30 m resolution by cubic convolution interpolation. | |
Chongqing statistical yearbook |
Ecosystem Services | Calculation Formulas | Parameters |
---|---|---|
Biodiversity | Qxj is the habitat quality of grid cell x in land use type j; Hj is the habitat suitability of land use type j; Dxj is the threat level of grid cell x in land use type j. The k is the half-saturation constant, which is often set as half of the maximum value of Dxj; z is the scaling parameter. R is the total number of threats; Yr indicates grid cells on threat r’s raster map; wr is threat r’s weight; ry is the relative impact of threat r in grid cell y; irxy is the impact of threat r from grid cell y on habitat in grid cell x; βx is the level of accessibility in grid cell x; and Sjr is the sensitivity of land use type j to threat r. | |
Carbon fixation | C is total carbon fixation; Cabove is carbon fixation in aboveground; Cbelow is carbon fixation in belowground; Csoil is carbon fixation in soil; and Cdead is carbon fixation of dead organic matter. | |
Soil conservation | SEDRET is the amount of soil conservation; RKLS is the potential soil loss; USLE is the actual soil loss; and SEDR is sediment retention. R is rainfall erosivity index; Pi is monthly precipitation; and P is annual precipitation. K is soil erodibility; KEPIC is in US customary units; SAN, SIL, CLA and C are the sand, silt, clay and organic carbon contents of soil, respectively. LS is slope length and steepness factor; Ai-in is the flow accumulation; D is the grid cell size; xi is the mean of aspect weighted by proportional outflow from grid cell i; θ is percentage slope; and m is length-slope exponent. C is the cover-management factor; fc is vegetation coverage. P is the support practice factor; and θ is percentage slope. | |
Water conservation | WC is the amount of water conservation; V is velocity coefficient; and Ksat is soil saturated hydraulic conductivity. TI is terrain index; DA is the number of grids in catchment area; Ds is the depth of soil; and θ is percentage slope. Yx is the water yield for grid cell x; AETx is the annual actual evapotranspiration; Px is the annual precipitation; PETx is the potential evapotranspiration; ωx is a nonphysical parameter of natural climate-soil properties; Kcℓx is plant evapotranspiration coefficient for each land use type; Z is the seasonal parameter; AWCx is the volumetric plant available water content; Rest.depthx is the root restricting layer depth; root.depthx is the vegetation rooting depth; and PAWCx is plant available water content. ET0 is average annual reference evapotranspiration; ETi, di, Di, Wti and Ti reference evapotranspiration, the number of days, sunshine duration, saturated water vapor density and temperature of month i, respectively. |
Fixed Effects | Biodiversity | Carbon Fixation | Soil Conservation | Water Conservation | ||||
---|---|---|---|---|---|---|---|---|
Estimate | Pr(>|t|) | Estimate | Pr(>|t|) | Estimate | Pr(>|t|) | Estimate | Pr(>|t|) | |
Cultivated land | 0.424 | <0.001 *** | 0.091 | 0.01 ** | −0.218 | <0.001 *** | 0.335 | <0.001 *** |
Forestland | 0.939 | <0.001 *** | 0.741 | <0.001 *** | 0.172 | <0.001 *** | 0.496 | <0.001 *** |
Grassland | 0.896 | <0.001 *** | 0.264 | <0.001 *** | −0.175 | <0.001 *** | 0.638 | <0.001 *** |
Waters | 0.878 | <0.001 *** | — | — | −0.269 | <0.001 *** | — | — |
Construction land | 0.056 | <0.001 *** | — | — | −0.221 | <0.001 *** | — | — |
MGDS | — | — | — | — | — | — | — | — |
RDCFM | — | — | 0.011 | 0.01 ** | 0.017 | 0.004 ** | — | — |
CEFR | — | — | — | — | — | — | — | — |
CER | — | — | 0.021 | <0.001 *** | — | — | −0.024 | <0.001 *** |
BLCP | 0.013 | <0.001 *** | 0.011 | 0.003 ** | — | — | −0.023 | <0.001 *** |
Intercept | 0.970 | <0.001 *** | 1.028 | <0.001 *** | 1.349 | <0.001 *** | 1.001 | <0.001 *** |
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Wang, F.; Yuan, X.; Zhou, L.; Liu, S.; Zhang, M.; Zhang, D. Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China. Remote Sens. 2021, 13, 4248. https://doi.org/10.3390/rs13214248
Wang F, Yuan X, Zhou L, Liu S, Zhang M, Zhang D. Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China. Remote Sensing. 2021; 13(21):4248. https://doi.org/10.3390/rs13214248
Chicago/Turabian StyleWang, Fang, Xingzhong Yuan, Lilei Zhou, Shuangshuang Liu, Mengjie Zhang, and Dan Zhang. 2021. "Detecting the Complex Relationships and Driving Mechanisms of Key Ecosystem Services in the Central Urban Area Chongqing Municipality, China" Remote Sensing 13, no. 21: 4248. https://doi.org/10.3390/rs13214248