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Article

Analysis of Driving Factors for Vegetation Ecological Quality Based on Bayesian Network

1
Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China
2
Nanchang Key Laboratory of Landscape Process and Territorial Spatial Ecological Restoration, East China University of Technology, Nanchang 330013, China
3
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China
4
Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(7), 1263; https://doi.org/10.3390/f15071263
Submission received: 25 June 2024 / Revised: 15 July 2024 / Accepted: 16 July 2024 / Published: 19 July 2024
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

Abstract

Vegetation is a crucial component of ecosystems, and understanding the drivers and spatial optimization patterns of its ecological quality is vital for environmental management in the middle reaches of the Yangtze River Urban Agglomeration. Traditional evaluations employing single indices may not fully capture the complexity of vegetation elements and require evaluation through various indicators. Therefore, this study introduced the Multi Criteria Vegetation Ecological Quality Index (VEQI), coupled with vegetation cover and vegetation ecological function indicators, to explore the driving factors of vegetation quality in the middle reaches of the Yangtze River and identify key areas where vegetation quality declines or improves. By constructing a Bayesian network for VEQI, we identified the driving variables that influence the index. Additionally, we delineated spatial optimization zones for VEQI. The results indicate that the VEQI exhibits a trend of transitioning from low values in urban centers to high values in suburban and rural areas. Over 20 years, the average VEQI of the study region ranged from 10.85% to 94.94%. Slope, DEM, and vegetation type were identified as significant drivers of VEQI, while precipitation, temperature, and nighttime light were considered secondary factors. Notably, areas in Hunan, Jiangxi, and Hubei provinces, especially the western part of Hunan, were pinpointed as spatial optimization regions. This research not only enhances the understanding of vegetation’s ecological quality in the urban agglomeration of the middle reaches of the Yangtze River but also provides scientific insights for the protection and management of vegetation.
Keywords: driving factors; spatial pattern optimization; Bayesian network; environmental management; middle reaches of the Yangtze River driving factors; spatial pattern optimization; Bayesian network; environmental management; middle reaches of the Yangtze River

Share and Cite

MDPI and ACS Style

Cai, J.; Wei, X.; Zhang, F.; Xia, Y. Analysis of Driving Factors for Vegetation Ecological Quality Based on Bayesian Network. Forests 2024, 15, 1263. https://doi.org/10.3390/f15071263

AMA Style

Cai J, Wei X, Zhang F, Xia Y. Analysis of Driving Factors for Vegetation Ecological Quality Based on Bayesian Network. Forests. 2024; 15(7):1263. https://doi.org/10.3390/f15071263

Chicago/Turabian Style

Cai, Jin, Xiaojian Wei, Fuqing Zhang, and Yuanping Xia. 2024. "Analysis of Driving Factors for Vegetation Ecological Quality Based on Bayesian Network" Forests 15, no. 7: 1263. https://doi.org/10.3390/f15071263

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