Analyzing Spatial-Temporal Change of Vegetation Ecological Quality and Its Influencing Factors in Anhui Province, Eastern China Using Multiscale Geographically Weighted Regression
Source abstract
Abstract
Vegetation is a crucial component of terrestrial ecology and plays a significant role in carbon sequestration. Monitoring changes in vegetation ecological quality has important guidance value for sustainable development. In this study, we investigated the spatial and temporal variation characteristics of Ecological Quality Index of Terrestrial Vegetation (EQI) in Anhui Province during the growing season from 2000 to 2020 using trend analysis, partial correlation analysis and bivariate spatial autocorrelation analysis. Based on the Multiscale Geographically Weighted Regression (MGWR), the spatial heterogeneity of the effects of average temperature, precipitation, elevation, slope, and human activity factors on EQI was explored. Our results showed an increasing trend in EQI during the growing season in Anhui Province from 2000 to 2020. The significantly increasing areas accounted for 43.49%, while the significantly decreasing areas accounted for 3.60%. EQI had a mostly positive correlation with precipitation and a negative correlation with average temperature (p < 0.1), showing a higher sensitivity to precipitation than to temperature. Additionally, EQI tended to increase initially and then decrease with increasing elevation and slope. Furthermore, our analysis revealed a significant negative spatial correlation between human activity intensity and EQI (p < 0.01). The bivariate global autocorrelation Moran index between EQI and human activity in 2000, 2005, 2010, 2015, and 2018 were −0.418, −0.427, −0.414, −0.487, and −0.470, respectively. We also found that the influencing factors explain 63–83% of the spatial variation of EQI, and the order of influence of factors on EQI is elevation > human activity > slope > average temperature > precipitation. MGWR results indicated that human activities and topographic factors had a stronger impact on EQI at the local scale, while climate factors tended to influence EQI at the global scale.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Land Use and Ecosystem Services · Spatial and Panel Data Analysis · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Key Laboratory of Aviation-Aerospace-Ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui Provincial Department of Education, Huainan 232001, China
Coal Industry Engineering Research Center of Collaborative Monitoring of Mining Area’s Environment and Disasters, Huainan 232001, China
Funding
Anhui Provincial Natural Science Foundation · National Natural Science Foundation of China · Research Fund for Doctoral Program of Anhui University of Science and Technology
References (49)
Show source-provided references
1. QX/T 494-2019 Interpretation of QX/T 494-2019, Grade of monitoring and evaluating for terrestrial vegetation meteorology and ecological quality
2. Terrestrial Vegetation in the Coupled Human-Earth System: Contributions of Remote Sensing10.1146/annurev.environ.33.020107.113339 ↗
3. Carbon sequestration potential of forest vegetation in China from 2003 to 2050: Predicting forest vegetation growth based on climate and the environment10.1016/j.jclepro.2019.119715 ↗
4. Spatial and temporal change of fractional vegetation cover in North-western China from 2000 to 201010.1002/gj.3030 ↗
5. Divergent vegetation responses to extreme spring and summer droughts in Southwestern China10.1016/j.agrformet.2019.107703 ↗
6. Li, Y., Wu, D., Yang, L., and Zhou, T. (2021). Declining Effect of Precipitation on the Normalized Difference Vegetation Index of Grasslands in the Inner Mongolian Plateau, 1982–2010. Appl. Sci., 11.10.3390/app11188766 ↗
7. Remote sensing cloud computing platform development and Earth science application10.11834/jrs.20210447 ↗
8. Gomes, V.C.F., Queiroz, G.R., and Ferreira, K.R. (2020). An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sens., 12.10.3390/rs12081253 ↗
9. Moore, R., and Hansen, M. (2011, January 5–9). Google Earth Engine: A New Cloud-Computing Platform for Global-Scale Earth Observation Data and Analysis. Proceedings of the American Geophysical Union Fall Meeting 2011, San Francisco, CA, USA.
10. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review10.1016/j.isprsjprs.2020.04.001 ↗
11. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review10.1109/JSTARS.2020.3021052 ↗
12. NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998-2007 in Inner Mongolia, China10.1002/joc.3543 ↗
13. Multi-perspective analysis of vegetation cover changes and driving factors of long time series based on climate and terrain data in Hanjiang River Basin, China10.1007/s12517-018-3756-3 ↗
14. Contributions of natural and human factors to increases in vegetation productivity in China10.1890/ES14-00394.1 ↗
15. The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor10.1016/j.ecolind.2018.04.067 ↗
16. Quantitative Assessment of the Contributions of Climate Change and Human Activities to Vegetation Variation in the Qinling Mountains10.3389/feart.2021.782287 ↗
17. Relative roles of climate changes and human activities in vegetation variables in Hainan Island
18. Spatial-temporal Changes of NDVI in Yellow River Basin and Its Dual Response to Climate Change and Human Activities During 2000–2018
19. The Human Footprint and the Last of the Wild: The Human Footprint Is a Global Map of Human Influence on the Land Surface, Which Suggests That Human Beings Are Stewards of Nature, Whether We like It or Not10.1641/0006-3568(2002)052[0891:THFATL]2.0.CO;2 ↗
20. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conser-vation10.1038/ncomms12558 ↗
21. A global record of annual terrestrial Human Footprint dataset from 2000 to 201810.1038/s41597-022-01284-8 ↗
22. Global terrestrial Human Footprint maps for 1993 and 200910.1038/sdata.2016.67 ↗
23. Wu, T., Zhou, L., Jiang, G., Meadows, M.E., Zhang, J., Pu, L., Wu, C., and Xie, X. (2021). Modelling Spatial Heterogeneity in the Effects of Natural and Socioeconomic Factors, and Their Interactions, on Atmospheric PM2.5 Concentrations in China from 2000–2015. Remote Sens., 13.10.3390/rs13112152 ↗
24. Yue, H., Duan, L., Lu, M., Huang, H., Zhang, X., and Liu, H. (2022). Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method. Atmosphere, 13.10.3390/atmos13040627 ↗
25. Nighttime Vitality and Its Relationship to Urban Diversity: An Exploratory Analysis in Shenzhen, China10.1109/JSTARS.2021.3130763 ↗
26. Zhou, M., Li, Y., and Zhang, F. (2022). Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph19159687 ↗
27. (2022, December 22). Grade of Monitoring and Evaluating for Terrestrial Vegetation Meteorology and Ecological Quality, Available online: https://www.cma.gov.cn/zfxxgk/gknr/flfgbz/bz/202209/t20220921_5099232.html.
28. Curve Fit: A pixel-level raster regression tool for mapping spatial patterns10.1111/2041-210X.12068 ↗
29. Responding Mechanism of Vegetation Cover to Climate Change and Human Activities in Southwest China from 2000 to 2020
30. Cumulative and time-lag effects of the main climate factors on natural vegetation across Siberia10.1016/j.ecolind.2021.108446 ↗
31. Spatial correlation characteristics of urbanization and land ecosystem service value in Wuhan Urban Agglomeration
32. The Local Indicators of Spatial Association—LISA10.1111/j.1538-4632.1995.tb00338.x ↗
33. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application10.1111/j.1538-4632.1995.tb00912.x ↗
34. Anselin, L., and Rey, S.J. (2014). Modern Spatial Econometrics in Practice: A Guide to Geoda, Geodaspace and PySAL, GeoDa Press LLC.
35. Spatial relationship between human activities and habitat quality in Shennongjia Forest Region from 1995 to 2015
36. Oshan, T.M., Li, Z., Kang, W., Wolf, L.J., and Fotheringham, A.S. (2019). Mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS Int. J. Geo Inf., 8.10.3390/ijgi8060269 ↗
37. On Hedonic Price of Second-Hand Houses in Beijing Based on Multi-Scale Geographically Weighted Regression: Scale Law of Spatial Heterogeneity
38. Analysis on the Normalized Difference Vegetation Index Change and Influence Factors in Anhui Province Based on Structural Equation Model
39. Long-term human expansion and the environmental impacts on the coastal zone of China
40. Zhao, Z., Li, T., Zhang, Y., Lü, D., Wang, C., Lü, Y., and Wu, X. (2022). Spatiotemporal Patterns and Driving Factors of Ecological Vulnerability on the Qinghai-Tibet Plateau Based on the Google Earth Engine. Remote Sens., 14.10.3390/rs14205279 ↗
41. Post-2020 biodiversity framework challenged by cropland expansion in protected areas10.1038/s41893-023-01093-w ↗
42. New ecological redline policy (ERP) to secure ecosystem services in China10.1016/j.landusepol.2015.09.002 ↗
43. Review and challenges of policies of environmental protection and sustainable development in China10.1016/j.jenvman.2007.06.019 ↗
44. Current situation and prospects of the studies of ecological industries and ecological products in eco-fragile areas10.1016/j.envres.2021.111613 ↗
45. Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001–201310.1007/s11442-017-1369-5 ↗
46. On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series10.1016/j.ecolmodel.2005.10.035 ↗
47. Li, M., Yan, Q., Li, G., Yi, M., and Li, J. (2022). Spatio-Temporal Changes of Vegetation Cover and Its Influencing Factors in Northeast China from 2000 to 2021. Remote Sens., 14.10.3390/rs14225720 ↗
48. Estimators of Relative Importance in Linear Regression Based on Variance Decomposition10.1198/000313007X188252 ↗
49. Factors contributing to spatial–temporal variations of observed oxygen concentration over the Qinghai-Tibetan Plateau10.1038/s41598-021-96741-6 ↗