Spatiotemporal Changes in Ecological Quality and Its Associated Driving Factors in Central Asia
Abstract
:1. Introduction
2. Study Area
3. Data and Methodology
3.1. Data Collection
3.2. Methods
3.2.1. Methods of RSEI
3.2.2. Spatial Auto-Correlation Analysis
3.2.3. GDM
4. Results
4.1. Dynamic Changes in the EEQ of CA
4.2. Spatiotemporal Characteristics of RSEI Evolution in CA
5. Discussion
5.1. Spatial Autocorrelation Analysis of EEQ
5.2. Impacts of Driving Factors on RSEI
5.2.1. Impacts of Natural Factors on RSEI
5.2.2. Impacts of Human Activities on RSEI
5.3. Limitations and Future Work
6. Conclusions
- (1)
- The RSEI values in CA during 2000, 2005, 2010, 2015, and 2020 were 0.379, 0.376, 0.349, 0.360, and 0.327, respectively, revealing that the EEQ was at a poor level and showed a deteriorating trend. Among the six regions of CA, although UZB and XJ had medium EEQ grades of fair, both of these regions showed a trend of improvement. KGZ and TJK had the best EEQ grades of moderate, KAZ had a medium EEQ grade of fair, and TKM had the worst EEQ grade of poor; all of these regions showed a trend of deterioration.
- (2)
- During 2000–2005, 2005–2010, 2010–2015, and 2015–2020, the unchanged/improved/deteriorated areas in CA were about 83.21/7.66%/9.13%, 77.28/6.68%/16.04%, 79.03/11.99%/8.98%, and 81.29/2.16%/16.55%, respectively, indicating that the overall EEQ in CA gradually deteriorated in recent years. The six regions of CA exhibited similar characteristics.
- (3)
- Analysis from Moran’s I index indicated that the spatial distribution of the EEQ in CA was clustered rather than random. Areas of H-H were mainly concentrated in mountainous areas, and L-L areas were mainly distributed in desert areas; the significant regions were mainly located in H-H and L-L areas, and most of them reached the significance level of 0.01, indicating that environmental quality had a strong correlation.
- (4)
- The results of the GDM revealed the influence of natural factors on the EEQ in CA. The findings showed that, although all factors had an impact on the RSEI in CA and its six regions, LST was the main factor. The impacts of various factors on the RSEI were not a simple superposition process but rather a mutual enhancement. The LST and NDBSI had a negative correlation with the RSEI, whereas the NDVI and WET had a positive correlation with the RSEI. In addition, human activities, such as population growth, overgrazing, and hydropower development, had an important impact on the EEQ.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
CA | Central Asia |
MODIS | Moderate Resolution Imaging Spectroradiometer |
GEE | Google Earth Engine |
EEQ | ecological environment quality |
RSEI | remote sensing ecological index |
H-H | high-high |
L-L | low-low |
H-L | high-low |
L-H | low-high |
XJ | Xinjiang Uygur Autonomous Region |
KAZ | Kazakhstan |
KGZ | Kyrgyzstan |
TJK | Tajikistan |
TKM | Turkmenistan |
UZB | Uzbekistan |
LST | land surface temperature |
NDBSI | normalized difference impervious surface index |
NDVI | normalized difference vegetation index |
WET | wetness |
PCA | principal component analysis |
GDM | geographical detector model |
PC1 | the first principal component |
PC2 | the second principal component |
EV1 | eigenvalue of principal component 1 |
EV2 | eigenvalue of principal component 2 |
ECR1 | contribution rate of principal component 1 |
ECR2 | contribution rate of principal component 2 |
SD | significant degeneration |
MD | mild degeneration |
IN | invariability |
MI | mild improvement |
SI | significant improvement |
NIi | the image standardization result of indicator |
Ii | the i pixel value of indicator |
Imax | the maximum values of indicator in the target year |
Imin | the minimum values of indicator in the target year |
f | the forward normalization of the four indicators |
RSEI0max | the maximum values of RSEI0 for the target year |
RSEI0min | the minimum values of RSEI0 for the target year |
References
- Li, D.; Wu, S.; Liu, L.; Zhang, Y.; Li, S. Vulnerability of the global terrestrial ecosystems to climate change. Glob. Chang. Biol. 2018, 24, 4095–4106. [Google Scholar] [CrossRef]
- Huang, J.; Zhang, G.; Zhang, Y.; Guan, X.; Wei, Y.; Guo, R. Global desertification vulnerability to climate change and human activities. Land Degrad. Dev. 2020, 31, 1380–1391. [Google Scholar] [CrossRef]
- Mahmoud, S.H.; Gan, T.Y. Impact of anthropogenic climate change and human activities on environment and ecosystem services in arid regions. Sci. Total Environ. 2018, 633, 1329–1344. [Google Scholar] [CrossRef] [PubMed]
- Yeilagi, S.; Rezapour, S.; Asadzadeh, F. Degradation of soil quality by the waste leachate in a Mediterranean semi-arid ecosystem. Sci. Rep. 2021, 11, 11390. [Google Scholar] [CrossRef] [PubMed]
- Li, C.; Wang, Y.; Wu, X.; Cao, H.; Li, W.; Wu, T. Reducing human activity promotes environmental restoration in arid and semi-arid regions: A case study in Northwest China. Sci. Total Environ. 2021, 768, 144525. [Google Scholar] [CrossRef]
- Wu, Z.; Lei, S.; Yan, Q.; Bian, Z.; Lu, Q. Landscape ecological network construction controlling surface coal mining effect on landscape ecology: A case study of a mining city in semi-arid steppe. Ecol. Indic. 2021, 133, 108403. [Google Scholar] [CrossRef]
- Kumar, R.; Pande, V.C.; Bhardwaj, A.K.; Dinesh, D.; Bhatnagar, P.R.; Dobhal, S.; Sharma, S.; Verma, K. Long-term impacts of afforestation on biomass production, carbon stock, and climate resilience in a degraded semi-arid ravine ecosystem of India. Ecol. Eng. 2022, 177, 106559. [Google Scholar] [CrossRef]
- Sullivan, C.A.; Skeffington, M.S.; Gormally, M.J.; Finn, J.A. The ecological status of grasslands on lowland farmlands in western Ireland and implications for grassland classification and nature value assessment. Biol. Conserv. 2010, 143, 1529–1539. [Google Scholar] [CrossRef]
- Ochoa-Gaona, S.; Kampichler, C.; de Jong, B.H.J.; Hernandez, S.; Geissen, V.; Huerta, E. A multi-criterion index for the evaluation of local tropical forest conditions in Mexico. For. Ecol. Manag. 2010, 260, 618–627. [Google Scholar] [CrossRef]
- Wu, X.X.; Lv, M.; Jin, Z.Y.; Michishita, R.; Chen, J.; Tian, H.Y.; Tu, X.B.; Zhao, H.M.; Niu, Z.G.; Chen, X.L.; et al. Normalized difference vegetation index dynamic and spatiotemporal distribution of migratory birds in the Poyang Lake wetland, China. Ecol. Indic. 2014, 47, 219–230. [Google Scholar] [CrossRef]
- Hu, X.; Xu, H. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecol. Indic. 2018, 89, 11–21. [Google Scholar] [CrossRef]
- Shan, W.; Jin, X.B.; Ren, J.; Wang, Y.C.; Xu, Z.G.; Fan, Y.T.; Gu, Z.M.; Hong, C.Q.; Lin, J.H.; Zhou, Y.K. Ecological environment quality assessment based on remote sensing data for land consolidation. J. Clean. Prod. 2019, 239, 118126. [Google Scholar] [CrossRef]
- Singh, R.K.; Khand, K.; Kagone, S.; Schauer, M.; Senay, G.B.; Wu, Z. A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data. Hydrol. Sci. J. J. Des Sci. Hydrol. 2020, 65, 2508–2519. [Google Scholar] [CrossRef]
- Wen, C.; Zhan, Q.; Zhan, D.; Zhao, H.; Yang, C. Spatiotemporal Evolution of Lakes under Rapid Urbanization: A Case Study in Wuhan, China. Water 2021, 13, 1171. [Google Scholar] [CrossRef]
- Xiong, Y.; Xu, W.; Lu, N.; Huang, S.; Wu, C.; Wang, L.; Dai, F.; Kou, W. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecol. Indic. 2021, 125, 107518. [Google Scholar] [CrossRef]
- Sun, D.; Zhang, J.; Zhu, C.; Hu, Y.; Zhou, L. An Assessment of China’s Ecological Environment Quality Change and Its Spatial Variation. Acta Geogr. Sin. 2012, 67, 1599–1610. [Google Scholar] [CrossRef]
- Xu, H. A remote sensing index for assessment of regional ecological changes. China Environ. Sci. 2013, 33, 889–897. [Google Scholar] [CrossRef]
- Yue, A.; Zhang, Z. Analysis and research on ecological situation change based on EI value. J. Green Sci. Technol. 2018, 14, 182–184. [Google Scholar] [CrossRef]
- Jing, Y.; Zhang, F.; He, Y.; Kung, H.-T.; Johnson, V.C.; Arikena, M. Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China. Ecol. Indic. 2020, 110, 105874. [Google Scholar] [CrossRef]
- Yuan, B.; Fu, L.; Zou, Y.; Zhang, S.; Chen, X.; Li, F.; Deng, Z.; Xie, Y. Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. J. Clean. Prod. 2021, 302, 126995. [Google Scholar] [CrossRef]
- Liu, C.; Yang, M.; Hou, Y.; Zhao, Y.; Xue, X. Spatiotemporal evolution of island ecological quality under different urban densities: A comparative analysis of Xiamen and Kinmen Islands, southeast China. Ecol. Indic. 2021, 124, 107438. [Google Scholar] [CrossRef]
- Boori, M.S.; Choudhary, K.; Paringer, R.; Kupriyanov, A. Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara, Russia. J. Environ. Manag. 2021, 285, 112138. [Google Scholar] [CrossRef] [PubMed]
- Ariken, M.; Zhang, F.; Liu, K.; Fang, C.L.; Kung, H.T. Coupling coordination analysis of urbanization and eco-environment in Yanqi Basin based on multi-source remote sensing data. Ecol. Indic. 2020, 114, 106331. [Google Scholar] [CrossRef]
- Yang, X.; Meng, F.; Fu, P.; Zhang, Y.; Liu, Y. Spatiotemporal change and driving factors of the Eco-Environment quality in the Yangtze River Basin from 2001 to 2019. Ecol. Indic. 2021, 131, 108214. [Google Scholar] [CrossRef]
- Yang, Z.; Tian, J.; Li, W.; Su, W.; Guo, R.; Liu, W. Spatio-temporal pattern and evolution trend of ecological environment quality in the Yellow River Basin. Acta Ecol. Sin. 2021, 41, 7627–7636. [Google Scholar] [CrossRef]
- Ji, J.; Tang, Z.; Zhang, W.; Liu, W.; Jin, B.; Xi, X.; Wang, F.; Zhang, R.; Guo, B.; Xu, Z.; et al. Spatiotemporal and Multiscale Analysis of the Coupling Coordination Degree between Economic Development Equality and Eco-Environmental Quality in China from 2001 to 2020. Remote Sens. 2022, 14, 737. [Google Scholar] [CrossRef]
- Cowan, P.J. Geographic usage of the terms middle Asia and Central Asia. J. Arid. Environ. 2007, 69, 359–363. [Google Scholar] [CrossRef]
- Yao, J.; Hu, W.; Chen, Y.; Huo, W.; Zhao, Y.; Mao, W.; Yang, Q. Hydro-climatic changes and their impacts on vegetation in Xinjiang, Central Asia. Sci. Total Environ. 2019, 660, 724–732. [Google Scholar] [CrossRef] [PubMed]
- Bai, J.; Shi, H.; Yu, Q.; Xie, Z.; Li, L.; Luo, G.; Jin, N.; Li, J. Satellite-observed vegetation stability in response to changes in climate and total water storage in Central Asia. Sci. Total Environ. 2019, 659, 862–871. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Y.; Zhang, L.; Fensholt, R.; Wang, K.; Vitkovskaya, I.; Tian, F. Climate Contributions to Vegetation Variations in Central Asian Drylands: Pre- and Post-USSR Collapse. Remote Sens. 2015, 7, 2449–2470. [Google Scholar] [CrossRef] [Green Version]
- Li, Z.; Chen, Y.N.; Li, W.H.; Deng, H.J.; Fang, G.H. Potential impacts of climate change on vegetation dynamics in Central Asia. J. Geophys. Res. Atmos. 2015, 120, 12345–12356. [Google Scholar] [CrossRef]
- Xu, H.; Wang, X.; Zhang, X. Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012. Int. J. Appl. Earth Obs. Geoinf. 2016, 52, 390–402. [Google Scholar] [CrossRef]
- Jiang, L.L.; Jiapaer, G.; Bao, A.M.; Guo, H.; Ndayisaba, F. Vegetation dynamics and responses to climate change and human activities in Central Asia. Sci. Total Environ. 2017, 599, 967–980. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.X.; Chen, Y.N.; Li, Z.; Fang, G.H.; Wang, F.; Liu, H.J. The impact of climate change and human activities on the Aral Sea Basin over the past 50 years. Atmos. Res. 2020, 245, 105125. [Google Scholar] [CrossRef]
- Berdimbetov, T.; Ma, Z.G.; Shelton, S.; Ilyas, S.; Nietullaeva, S. Identifying Land Degradation and its Driving Factors in the Aral Sea Basin from 1982 to 2015. Front. Earth Sci. 2021, 9, 834. [Google Scholar] [CrossRef]
- Lioubimtseva, E.; Cole, R.; Adams, J.; Kapustin, G. Impacts of climate and land-cover changes in arid lands of Central Asia. J. Arid. Environ. 2005, 62, 285–308. [Google Scholar] [CrossRef]
- Qin, J.; Hao, X.; Hua, D.; Hao, H. Assessment of ecosystem resilience in Central Asia. J. Arid. Environ. 2021, 195, 104625. [Google Scholar] [CrossRef]
- He, Y.; You, N.; Cui, Y.; Xiao, T.; Hao, Y.; Dong, J. Spatio-temporal changes in remote sensing-based ecological index in China since 2000. J. Nat. Resour. 2021, 36, 1176–1185. [Google Scholar] [CrossRef]
- Fan, G.Y.; Cowley, J.M. Auto-correlation analysis of high resolution electron micrographs of near-amorphous thin films. Ultramicroscopy 1985, 17, 345–355. [Google Scholar] [CrossRef]
- Martin, D. An assessment of surface and zonal models of population. Int. J. Geogr. Inf. Syst. 1996, 10, 973–989. [Google Scholar] [CrossRef]
- Liu, X.; Wang, H.; Wang, X.; Bai, M.; He, D. Driving factors and their interactions of carabid beetle distribution based on the geographical detector method. Ecol. Indic. 2021, 133, 108393. [Google Scholar] [CrossRef]
- Zhao, F.; Zhang, S.; Du, Q.; Ding, J.; Luan, G.; Xie, Z. Assessment of the sustainable development of rural minority settlements based on multidimensional data and geographical detector method: A case study in Dehong, China. Socio-Econ. Plan. Sci. 2021, 78, 101066. [Google Scholar] [CrossRef]
- Li, L.; Fan, Z.; Feng, W.; Yuxin, C.; Keyu, Q. Coupling coordination degree spatial analysis and driving factor between socio-economic and eco-environment in northern China. Ecol. Indic. 2022, 135, 108555. [Google Scholar] [CrossRef]
- Xiao, Z.; Liu, R.; Gao, Y.; Yang, Q.; Chen, J. Spatiotemporal variation characteristics of ecosystem health and its driving mechanism in the mountains of southwest China. J. Clean. Prod. 2022, 345, 131138. [Google Scholar] [CrossRef]
- Wang, J.; Xu, C. Geodetector: Principle and prospective. Acta Geogr. Sin. 2017, 72, 116–134. [Google Scholar] [CrossRef]
- Song, H.M.; Xue, L. Dynamic monitoring and analysis of ecological environment in Weinan City, Northwest China based on RSEI model. Chin. J. Appl. Ecol. 2016, 27, 3913–3919. [Google Scholar] [CrossRef]
- Jiang, C.L.; Wu, L.; Liu, D.; Wang, S.M. Dynamic monitoring of eco-environmental quality in arid desert area by remote sensing: Taking the Gurbantunggut Desert China as an example. Chin. J. Appl. Ecol. 2019, 30, 877–883. [Google Scholar] [CrossRef]
- Eyring, V.; Cox, P.M.; Flato, G.M.; Gleckler, P.J.; Abramowitz, G.; Caldwell, P.; Collins, W.D.; Gier, B.K.; Hall, A.D.; Hoffman, F.M.; et al. Taking climate model evaluation to the next level. Nat. Clim. Chang. 2019, 9, 102–110. [Google Scholar] [CrossRef] [Green Version]
- Sun, C.; Li, X.; Zhang, W.; Li, X. Evolution of ecological security in the tableland region of the Chinese Loess Plateau Using a remote-sensing-based index. Sustainability 2020, 12, 3489. [Google Scholar] [CrossRef] [Green Version]
- Wen, X.; Ming, Y.; Gao, Y.; Hu, X. Dynamic Monitoring and Analysis of Ecological Quality of Pingtan Comprehensive Experimental Zone, a New Type of Sea Island City, Based on RSEI. Sustainability 2020, 12, 21. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Q.; Guo, J.; Guo, X.; Xu, Z.; Ding, H.; Han, Y. Spatial variation of ecological environment quality and its influencing factors in Poyang Lake area, Jiangxi, China. Chin. J. Appl. Ecol. 2019, 30, 4108–4116. [Google Scholar] [CrossRef]
- Wang, S.; Zhang, X.; Zhu, T.; Yang, W.; Zhao, J. Assessment of ecological environment quality in the Changbai Mountain Nature Reserve based on remote sensing technology. Prog. Geogr. 2016, 35, 1269–1278. [Google Scholar] [CrossRef]
- Loboda, T.V.; Giglio, L.; Boschetti, L.; Justice, C.O. Regional fire monitoring and characterization using global NASA MODIS fire products in dry lands of Central Asia. Front. Earth Sci. 2012, 6, 196–205. [Google Scholar] [CrossRef]
- Chen, T.; Tang, G.; Yuan, Y.; Guo, H.; Xu, Z.; Jiang, G.; Chen, X. Unraveling the relative impacts of climate change and human activities on grassland productivity in Central Asia over last three decades. Sci. Total Environ. 2020, 743, 140649. [Google Scholar] [CrossRef] [PubMed]
- Beek, T.A.D.; Voss, F.; Florke, M. Modelling the impact of Global Change on the hydrological system of the Aral Sea basin. Phys. Chem. Earth 2011, 36, 684–695. [Google Scholar] [CrossRef]
- Hall, J.W.; Grey, D.; Garrick, D.; Fung, F.; Brown, C.; Dadson, S.J.; Sadoff, C.W. Coping with the curse of freshwater variability. Science 2014, 346, 429–430. [Google Scholar] [CrossRef]
- Wang, J.; Liu, D.; Ma, J.; Cheng, Y.; Wang, L. Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin. J. Arid. Land 2021, 13, 40–55. [Google Scholar] [CrossRef]
- Huang, W.; Duan, W.; Chen, Y. Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes. Sci. Total Environ. 2021, 784, 147193. [Google Scholar] [CrossRef]
- Li, J.; Chen, Y.; Xu, C.; Li, Z. Evaluation and analysis of ecological security in arid areas of Central Asia based on the emergy ecological footprint (EEF) model. J. Clean. Prod. 2019, 235, 664–677. [Google Scholar] [CrossRef]
- Bi, X.; Chang, B.; Hou, F.; Yang, Z.; Fu, Q.; Li, B. Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang. Int. J. Environ. Res. Public Health 2021, 18, 7111. [Google Scholar] [CrossRef]
- Yang, L.; Shi, L.; Wei, J.; Wang, Y. Spatiotemporal evolution of ecological environment quality in arid areas based on the remote sensing ecological distance index: A case study of Yuyang district in Yulin city, China. Open Geosci. 2021, 13, 1701–1710. [Google Scholar] [CrossRef]
- Sun, L.; Yu, Y.; Gao, Y.; He, J.; Yu, X.; Malik, I.; Wistuba, M.; Yu, R. Remote Sensing Monitoring and Evaluation of the Temporal and Spatial Changes in the Eco-Environment of a Typical Arid Land of the Tarim Basin in Western China. Land 2021, 10, 868. [Google Scholar] [CrossRef]
- Wu, S.; Gao, X.; Lei, J.; Zhou, N.; Guo, Z.; Shang, B. Ecological environment quality evaluation of the Sahel region in Africa based on remote sensing ecological index. J. Arid. Land 2022, 14, 14–33. [Google Scholar] [CrossRef]
- Chen, Y.N.; Li, W.H.; Deng, H.J.; Fang, G.H.; Li, Z. Changes in Central Asia’s Water Tower: Past, Present and Future. Sci. Rep. 2016, 6, 35458. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Liu, W.; Cai, Y.; Khan, S.U.; Zhao, M. Decoupling analysis of water use and economic development in arid region of China-Based on quantity and quality of water use. Sci. Total Environ. 2021, 761, 143275. [Google Scholar] [CrossRef]
- Wang, X.Y.; Peng, S.Z.; Ling, H.B.; Xu, H.L.; Ma, T.T. Do Ecosystem Service Value Increase and Environmental Quality Improve due to Large-Scale Ecological Water Conveyance in an Arid Region of China? Sustainability 2019, 11, 6586. [Google Scholar] [CrossRef] [Green Version]
- He, T.M.; Wang, C.X.; Wang, Z.L.; He, X.L.; Liu, H.G.; Zhang, J. Assessing the Agricultural Water Savings-Economy-Ecological Environment System in an Arid Area of Northwest China Using a Water Rights Transaction Model. Water 2021, 13, 1233. [Google Scholar] [CrossRef]
Year | Indicator | CA | KAZ | KGZ | TJK | TKM | UZB | XJ |
---|---|---|---|---|---|---|---|---|
2000 | LST | −0.87 | −0.71 | 0.97 | 0.94 | −0.84 | −0.75 | −0.95 |
NDBSI | 0.00 | −0.15 | 0.00 | 0.00 | −0.04 | −0.05 | −0.01 | |
NDVI | 0.45 | 0.66 | 0.00 | 0.13 | 0.27 | 0.44 | 0.27 | |
WET | 0.19 | 0.21 | −0.23 | −0.33 | 0.48 | 0.50 | 0.16 | |
EV1 | 0.04 | 0.05 | 0.05 | 0.01 | 0.03 | 0.06 | 0.05 | |
EV2 | 0.00 | 0.02 | 0.02 | 0.00 | 0.01 | 0.01 | 0.01 | |
ECR1 | 79.51 | 85.67 | 68.30 | 70.54 | 62.95 | 74.12 | 80.35 | |
ECR2 | 17.05 | 8.92 | 26.96 | 22.22 | 21.51 | 16.30 | 15.66 | |
2005 | LST | −0.82 | −0.70 | −0.97 | 0.93 | −0.80 | −0.76 | −0.92 |
NDBSI | 0.00 | −0.01 | 0.00 | 0.01 | −0.01 | −0.02 | −0.01 | |
NDVI | 0.53 | 0.68 | −0.07 | 0.12 | 0.32 | 0.47 | 0.34 | |
WET | 0.21 | 0.22 | 0.23 | −0.36 | 0.51 | 0.46 | 0.21 | |
EV1 | 0.04 | 0.05 | 0.06 | 0.01 | 0.03 | 0.06 | 0.05 | |
EV2 | 0.00 | 0.03 | 0.02 | 0.00 | 0.01 | 0.02 | 0.01 | |
ECR1 | 76.97 | 87.52 | 60.84 | 69.79 | 65.31 | 78.67 | 76.00 | |
ECR2 | 19.57 | 8.96 | 34.55 | 23.93 | 19.06 | 13.77 | 18.86 | |
2010 | LST | −0.87 | −0.70 | −0.95 | 0.90 | −0.72 | −0.70 | −0.92 |
NDBSI | 0.00 | −0.12 | 0.00 | 0.01 | −0.06 | −0.02 | 0.00 | |
NDVI | 0.44 | 0.65 | −0.15 | 0.23 | 0.32 | 0.53 | 0.33 | |
WET | 0.24 | 0.27 | 0.29 | −0.37 | 0.61 | 0.48 | 0.21 | |
EV1 | 0.03 | 0.05 | 0.07 | 0.02 | 0.04 | 0.06 | 0.04 | |
EV2 | 0.00 | 0.03 | 0.03 | 0.00 | 0.01 | 0.02 | 0.01 | |
ECR1 | 76.58 | 85.33 | 60.63 | 67.51 | 67.26 | 78.84 | 75.82 | |
ECR2 | 19.61 | 10.04 | 33.76 | 26.80 | 20.57 | 14.08 | 19.30 | |
2015 | LST | −0.83 | −0.72 | −0.95 | 0.93 | −0.74 | −0.70 | −0.89 |
NDBSI | 0.00 | −0.03 | 0.00 | 0.01 | −0.01 | 0.00 | −0.01 | |
NDVI | 0.49 | 0.65 | −0.04 | 0.12 | 0.35 | 0.51 | 0.38 | |
WET | 0.25 | 0.24 | 0.31 | −0.34 | 0.58 | 0.50 | 0.24 | |
EV1 | 0.04 | 0.05 | 0.06 | 0.02 | 0.04 | 0.06 | 0.05 | |
EV2 | 0.00 | 0.02 | 0.02 | 0.01 | 0.01 | 0.02 | 0.01 | |
ECR1 | 77.97 | 89.10 | 64.07 | 65.64 | 66.14 | 78.87 | 74.23 | |
ECR2 | 18.01 | 7.42 | 29.99 | 27.60 | 22.03 | 13.01 | 20.35 | |
2020 | LST | −0.78 | −0.64 | −0.87 | −0.84 | −0.55 | −0.58 | −0.83 |
NDBSI | 0.00 | −0.19 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
NDVI | 0.45 | 0.63 | −0.04 | −0.07 | 0.27 | 0.43 | 0.39 | |
WET | 0.42 | 0.39 | 0.49 | 0.53 | 0.79 | 0.69 | 0.41 | |
EV1 | 0.04 | 0.06 | 0.07 | 0.02 | 0.05 | 0.06 | 0.05 | |
EV2 | 0.00 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 | |
ECR1 | 76.11 | 82.65 | 63.50 | 70.12 | 70.55 | 79.99 | 72.00 | |
ECR2 | 14.71 | 8.73 | 25.29 | 18.84 | 21.83 | 12.38 | 15.91 |
RSEI Level | 2000 | 2005 | 2010 | 2015 | 2020 | |
---|---|---|---|---|---|---|
CA | poor | 20.90% | 21.75% | 22.16% | 23.94% | 28.50% |
fair | 37.67% | 38.37% | 45.56% | 39.71% | 39.91% | |
moderate | 29.11% | 27.00% | 21.02% | 23.99% | 23.59% | |
good | 11.66% | 11.89% | 10.27% | 11.52% | 7.84% | |
excellent | 0.66% | 0.99% | 0.98% | 0.85% | 0.17% | |
KAZ | poor | 2.99% | 3.25% | 5.18% | 6.31% | 11.66% |
fair | 47.22% | 51.74% | 66.79% | 55.85% | 56.11% | |
moderate | 39.98% | 34.42% | 22.89% | 28.18% | 27.14% | |
good | 9.25% | 9.43% | 4.32% | 8.54% | 4.85% | |
excellent | 0.57% | 1.17% | 0.82% | 1.11% | 0.24% | |
KGZ | poor | 0.00% | 0.00% | 0.00% | 0.02% | 0.09% |
fair | 10.21% | 7.45% | 4.49% | 11.50% | 11.59% | |
moderate | 31.17% | 32.88% | 27.90% | 34.97% | 36.11% | |
good | 55.76% | 56.23% | 60.99% | 51.62% | 51.60% | |
excellent | 2.86% | 3.44% | 6.61% | 1.88% | 0.61% | |
TJK | poor | 4.61% | 3.02% | 2.78% | 4.59% | 6.06% |
fair | 24.18% | 20.28% | 15.80% | 20.61% | 25.54% | |
moderate | 49.93% | 48.63% | 42.35% | 46.68% | 49.84% | |
good | 21.25% | 27.84% | 37.39% | 28.00% | 18.54% | |
excellent | 0.03% | 0.23% | 1.68% | 0.13% | 0.02% | |
TKM | poor | 79.86% | 77.01% | 82.95% | 82.09% | 84.83% |
fair | 17.89% | 19.26% | 13.87% | 14.41% | 12.70% | |
moderate | 2.24% | 3.72% | 3.16% | 3.46% | 2.36% | |
good | 0.01% | 0.02% | 0.02% | 0.04% | 0.10% | |
excellent | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | |
UZB | poor | 48.22% | 53.36% | 57.85% | 59.00% | 63.25% |
fair | 35.90% | 26.75% | 22.48% | 23.25% | 19.78% | |
moderate | 15.34% | 18.36% | 17.84% | 16.69% | 16.22% | |
good | 0.54% | 1.52% | 1.83% | 1.06% | 0.74% | |
excellent | 0.00% | 0.00% | 0.01% | 0.00% | 0.00% | |
XJ | poor | 29.74% | 31.72% | 26.85% | 30.89% | 35.46% |
fair | 32.14% | 29.69% | 32.60% | 29.29% | 30.67% | |
moderate | 20.71% | 21.42% | 21.59% | 22.00% | 22.45% | |
good | 16.40% | 16.13% | 17.82% | 16.99% | 11.29% | |
excellent | 1.01% | 1.03% | 1.13% | 0.83% | 0.13% |
Period | Region | SD | MD | IN | MI | SI |
---|---|---|---|---|---|---|
2000–2005 | CA | 0.00% | 6.55% | 83.97% | 9.48% | 0.00% |
KAZ | 0.00% | 11.62% | 80.40% | 7.98% | 0.00% | |
KGZ | 0.00% | 5.40% | 84.33% | 10.27% | 0.00% | |
TJK | 0.00% | 2.84% | 80.24% | 16.92% | 0.00% | |
TKM | 0.00% | 1.11% | 93.32% | 5.57% | 0.00% | |
UZB | 0.00% | 10.57% | 79.36% | 10.07% | 0.00% | |
XJ | 0.00% | 7.76% | 86.16% | 6.08% | 0.00% | |
2005–2010 | CA | 0.00% | 8.12% | 83.04% | 8.83% | 0.00% |
KAZ | 0.00% | 27.78% | 68.50% | 3.72% | 0.00% | |
KGZ | 0.00% | 1.55% | 83.09% | 15.36% | 0.00% | |
TJK | 0.00% | 0.75% | 81.07% | 18.17% | 0.00% | |
TKM | 0.00% | 7.34% | 91.95% | 0.71% | 0.00% | |
UZB | 0.00% | 7.27% | 90.45% | 2.28% | 0.00% | |
XJ | 0.00% | 4.05% | 83.20% | 12.75% | 0.00% | |
2010–2015 | CA | 0.00% | 12.64% | 81.90% | 5.46% | 0.00% |
KAZ | 0.00% | 6.73% | 73.57% | 19.70% | 0.00% | |
KGZ | 0.00% | 27.04% | 72.00% | 0.96% | 0.00% | |
TJK | 0.00% | 21.29% | 78.35% | 0.36% | 0.00% | |
TKM | 0.00% | 1.83% | 95.05% | 3.13% | 0.00% | |
UZB | 0.01% | 6.26% | 91.52% | 2.21% | 0.00% | |
XJ | 0.00% | 12.69% | 80.90% | 6.41% | 0.00% | |
2015–2020 | CA | 0.00% | 13.14% | 84.42% | 2.44% | 0.00% |
KAZ | 0.00% | 18.37% | 79.61% | 2.01% | 0.00% | |
KGZ | 0.00% | 8.65% | 86.01% | 5.34% | 0.00% | |
TJK | 0.00% | 19.40% | 78.78% | 1.82% | 0.00% | |
TKM | 0.00% | 5.15% | 93.60% | 1.25% | 0.00% | |
UZB | 0.00% | 7.25% | 91.03% | 1.73% | 0.00% | |
XJ | 0.00% | 20.04% | 77.49% | 2.47% | 0.00% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Xia, Q.-Q.; Chen, Y.-N.; Zhang, X.-Q.; Ding, J.-L. Spatiotemporal Changes in Ecological Quality and Its Associated Driving Factors in Central Asia. Remote Sens. 2022, 14, 3500. https://doi.org/10.3390/rs14143500
Xia Q-Q, Chen Y-N, Zhang X-Q, Ding J-L. Spatiotemporal Changes in Ecological Quality and Its Associated Driving Factors in Central Asia. Remote Sensing. 2022; 14(14):3500. https://doi.org/10.3390/rs14143500
Chicago/Turabian StyleXia, Qian-Qian, Ya-Ning Chen, Xue-Qi Zhang, and Jian-Li Ding. 2022. "Spatiotemporal Changes in Ecological Quality and Its Associated Driving Factors in Central Asia" Remote Sensing 14, no. 14: 3500. https://doi.org/10.3390/rs14143500