Datasets
Standard Dataset
Ecological carrying capacity assessment
- Citation Author(s):
-
FunanLiu
Xinjiang University - Submitted by:
- Funan Liu
- Last updated:
- Sun, 03/23/2025 - 01:26
- DOI:
- 10.21227/z7s9-t949
- License:
32 Views- Categories:
- Keywords:
Abstract
Ecological carrying capacity (ECC) is central to assessing the sustainability of ecosystems, aiming to quantify the limits of natural systems to support human activities while maintaining biodiversity and resource regeneration. To assess ECC, earlier studies typically used the analytic hierarchy process (AHP) method for modeling. In this study, we developed an AHP-EW method based on a combination of AHP and entropy weight method, which considered important indicators including land use, vegetation, soil, location, topography, climate, and socio-economics, and constructed an ECC evaluation system. The new AHP-EW method was applied to analyze the spatiotemporal ECC patterns in Urumqi from 2000 to 2020. The results showed a general decreasing trend in ECC during the period 2000-2020. Among them, the ECC decreased significantly by 19.05% from 2000 to 2010, while after 2010, the decrease slowed down to 14.12%, owing to the implementation of ecological conservation policies. In addition, Midong District, Dabancheng District, and Urumqi County had worse ECC. Still, in general, the distribution of ECC in each district and county showed a trend of decreasing in areas with low ECC and increasing in areas with high ECC. Cluster analysis showed that ECC improved in ecological reserve areas, while some built-up areas showed a decrease in ECC due to economic development and human activities. Driving factors analysis shows that NDVI, climate change, and land use conversion are the key factors influencing the change of ECC in Urumqi. This study provides new ideas and technical support for ECC assessment in the global arid areas, which can help formulate more effective ecological protection strategies and promote the healthy and stable development of regional ecosystems.
Table Ⅲ summarizes the details of the data sources for ECC evaluation indicators. Land use data were used from the China Land Cover Dataset (CLCD) for the years 2000, 2010, and 2020 with an accuracy of 80%, based on which the land use intensity of the region was calculated. To investigate the relationship between ECC and vegetation, MOD13Q1 data was used to obtain the annual mean NDVI for the study area. We processed VV + VH polarization data using Sentinel-1A images to invert soil moisture [53, 54]. Soil erosion data were obtained from the Scientific Data Bank, covering 2000, 2010, and 2020, with a resolution of 30 m.
TABLE Ⅲ
Data sources for ECC evaluation indicators
Name
Time
Resolution
(m)
Source
Land use data
Land use intensity (LUI)
2000, 2010, 2020
30
Zenodo (zenodo.org)
Vegetation
NDVI
2000, 2010, 2020
250
Soil data
Soil moisture
2000, 2010, 2020
30
NASA Sentinel-1 GRD (nasa.gov)
Soil erosion
2000, 2010, 2020
30
Scientific Data Bank (scidb.cn)
Location factors
Distance to river
2020
30
National Catalogue Service For Geographic Information (webmap.cn)
Terrain
DEM
2020
30
Slope
2020
30
Climate
Precipitation
2000, 2010, 2020
1000
Resources and Environmental Science Data Center (resdc.cn)
Temperature
2000, 2010, 2020
1000
Resources and Environmental Science Data Center (resdc.cn)
Aridity index (AI)
2000, 2010, 2020
500
Nighttime light
2000, 2010
1000
DMSP Nighttime Lights (eogdata.mines.edu)
2020
750
VIRS Nighttime Lights (eogdata.mines.edu)
GDP
2000, 2010, 2020
1000
Resources and Environmental Science Data Center (resdc.cn)
Population
2000, 2010, 2020
1000
Resources and Environmental Science Data Center (resdc.cn)
Location factors were obtained from the National Catalogue Service For Geographic Information to explore the influence of the river on ECC. DEM was obtained from NASA, and slope data were calculated from DEM using the slope analysis tool in ArcMap. The Resources and Environmental Science Data Center provided average annual precipitation and average annual temperature. The aridity index was obtained from NASA MOD16A2. For social economy data, nighttime lighting data for 2000 and 2010 was from DMSP-OLS, and data for 2020 was from NPP-VIIRS data. GDP and population data was obtained from the Resources and Environmental Science Data Center for the years 2000, 2010, and 2020. All used MODIS data were reprojected, mosaicked, and formatted using MRT (MODIS Reprojection Tool).
