Scales and Historical Evolution: Methods to Reveal the Relationships between Ecosystem Service Bundles and Socio-Ecological Drivers—A Case Study of Dalian City, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Conceptual Framework
2.3. Data Sources
2.4. Mapping ESs at Multiple Spatial Scales
2.5. Identify ES Bundles at Multiple Spatial Scales
2.6. Identification of Drivers of ES Bundles
3. Results
3.1. Spatial Distribution Characteristics of ESs at Different Spatial Scales
3.2. Historical Patterns and Dynamics of ES Bundles at Different Spatial Scales
3.3. Determining Socio-Ecological Drivers for ES Bundles at Different Spatial Scales
4. Discussion
4.1. Model Validation and Uncertainty Analysis
4.2. Influencing Mechanism of ES Bundles
4.3. Guidelines for Landscape Planning and Management
4.4. Limitations and Future Directions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Category | ES Types | Data Source | Usage Details and Resolution | |||||
---|---|---|---|---|---|---|---|---|
FS | HQ | SC | LA | CSOP | WC | |||
Land-use data | √ | √ | √ | √ | √ | √ | Land-use data was obtained by interpreting Landsat TM/ET/OLI data from the USGS website (accessed on 3 November 2020) (https://usgs.gov/landsat, accessed on 5 December 2019) | Interpreted and obtained thirteen types of land-use in Dalian from 2005 to 2015 (30 m × 30 m) |
MODIS data | √ | Normalized difference vegetation index (NDVI) (http://lpdaac.usgs.gov/products/mod13q1v006/, accessed on 15 March 2020) Net primary production (NPP) (http://lpdaac.usgs.gov/products/mod17a3hv006/, accessed on 26 March 2020) | NDVI (MODIS13Q1 dataset) (250 m × 250 m) | |||||
NPP (MODIS17A3 dataset) (500 m × 500 m) | ||||||||
Digital Elevation Model (DEM) data | √ | √ | Geospatial data cloud site (http://www.gscloud.cn/, accessed on 14 December 2019) | Based on the digital elevation model (DEM) data, extracted the slope and slope length by hydrology modeling (30 m × 30 m) | ||||
Soil data | √ | √ | China soil map based on harmonized world soil database (HWSD) (v1.1) (http://www.ncdc.ac.cn/portal/, accessed on 25 January 2020) | Including current data on silt, clay, sand, and organic carbon (1 km × 1 km) | ||||
Meteorological data | √ | √ | √ | China meteorological data network (http://data.cma.cn/, accessed on 15 January 2020) | Includes meteorological data such as precipitation, evaporation, average temperature, wind speed, and solar radiation from thirteen weather stations in and around Dalian (Text data–daily and monthly) | |||
Socio economic data | √ | √ | Statistical yearbook of Dalian (http://stats.dl.gov.cn/, accessed on 20 November 2020) | Including annual food production, tourism income in the study area (Text data–yearly) |
ESs | Description | Unit | Evaluation Methods and Key References |
---|---|---|---|
FS (Provisioning services) | Crops (cereals, fruits, vegetables), livestock products (meat, eggs, milk), aquatic products (shrimp, crab, fish) | (t/hm2·a) | Food yield per unit area is assigned to the corresponding land-use grid [42,43]. |
HQ (Supporting services) | Distribution of habitat quality was quantified by combining the sensitivity of the landscape type and the intensity of external threats | Index (0–1) | based on the habitat quality module in the integrated valuation of ecosystem services and trade-offs (InVEST) model [44]. |
SC (Supporting services) | Quantification of the supply of soil conservation caused by vegetation through the effect of vegetation on reducing soil loss and sediment accumulation | (t/hm2·a) | Use of the revised universal soil loss equation (RUSLE) model to estimate potential soil erosion and actual soil erosion [28]. |
CSOP (Regulating services) | Use of NPP data based on the principle of photosynthesis, in which 1 unit of organic matter can fix 1.63 units of carbon dioxide and production 1.2 units of oxygen | (g C/m2·a) | Estimation of NPP based on Carnegie–Ames–Stanford Approach (CASA) model [45,46]. |
WC (Regulating services) | Adoption of the principle of water balance, and calculate the flow rate coefficient, soil permeability, soil conservation, and hydraulic conductivity of Dalian to obtain water conservation | (mm·a) | Use of the InVEST model to quantify water yield [47,48]. |
LA (Cultural services) | Considering that the tourism industry can indirectly reflect landscape aesthetics, tourism income is used to characterize the service value of landscape aesthetics | (yuan/hm2·a) | The equivalent value per unit area method was used to assign the revised tourism revenue per unit area to the landscape category. |
Category | Driving Factors | Spatial Resolution | Source |
---|---|---|---|
Natural factors | PRE—annual average precipitation | 1 km × 1 km | http://data.cma.cn/, accessed on 15 January 2020 |
MT—mean temperature | 1 km × 1 km | http://data.cma.cn/, accessed on 15 January 2020 | |
TSR—total solar radiation | 1 km × 1 km | http://data.cma.cn/, accessed on 15 January 2020 | |
NDVI—normalized difference vegetation index | 1 km × 1 km | https://www.nasa.gov/, accessed on 15 March 2020 | |
SLOPE—terrain slope | 1 km × 1 km | http://www.gscloud.cn/, accessed on 16 December 2019 | |
DEM—digital elevation model | 1 km × 1 km | http://www.gscloud.cn/, accessed on 14 December 2019 | |
CLAY—percentage of clay in soil | 1 km × 1 km | http://westdc.westgis.ac.cn, accessed on 25 January 2020 | |
OM—percentage of organic matter in soil | 1 km × 1 km | http://westdc.westgis.ac.cn, accessed on 25 January 2020 | |
SAND—percentage of sand in soil | 1 km × 1 km | http://westdc.westgis.ac.cn, accessed on 25 January 2020 | |
SILT—percentage of silt in soil | 1 km × 1 km | http://westdc.westgis.ac.cn, accessed on 25 January 2020 | |
Human factors | POP—population density | 1 km × 1 km | http://www.resdc.cn/, accessed on 20 November 2020 |
UR—urbanization rate | 1 km × 1 km | http://www.resdc.cn/, accessed on 20 November 2020 | |
GDP—GDP per unit area | 1 km × 1 km | http://www.resdc.cn/, accessed on 20 November 2020 | |
LUI—land-use intensity | 1 km × 1 km | https://usgs.gov/landsat, accessed on 20 November 2020 |
Judgment Criteria | Type of Interaction |
---|---|
q(X1∩X2) < Min(q(X1), q(X2)) | Non-linear reduction |
Min(q(X1), q(X2)) < q(X1∩X2) < Max(q(X1), q(X2)) | Single-factor nonlinearity reduction |
q(X1∩X2) > Max(q(X1), q(X2)) | Double factor enhancement |
q(X1∩X2) > q(X1) + q(X2) | Non-linear enhancement |
q(X1∩X2) = q(X1) + q(X2) | Independent |
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Yan, X.; Li, X.; Liu, C.; Li, J.; Zhong, J. Scales and Historical Evolution: Methods to Reveal the Relationships between Ecosystem Service Bundles and Socio-Ecological Drivers—A Case Study of Dalian City, China. Int. J. Environ. Res. Public Health 2022, 19, 11766. https://doi.org/10.3390/ijerph191811766
Yan X, Li X, Liu C, Li J, Zhong J. Scales and Historical Evolution: Methods to Reveal the Relationships between Ecosystem Service Bundles and Socio-Ecological Drivers—A Case Study of Dalian City, China. International Journal of Environmental Research and Public Health. 2022; 19(18):11766. https://doi.org/10.3390/ijerph191811766
Chicago/Turabian StyleYan, Xiaolu, Xinyuan Li, Chenghao Liu, Jiawei Li, and Jingqiu Zhong. 2022. "Scales and Historical Evolution: Methods to Reveal the Relationships between Ecosystem Service Bundles and Socio-Ecological Drivers—A Case Study of Dalian City, China" International Journal of Environmental Research and Public Health 19, no. 18: 11766. https://doi.org/10.3390/ijerph191811766