Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China
Zhiming Xia,
Kaitao Liao,
Liping Guo,
Bin Wang,
Hongsheng Huang,
Xiulong Chen,
Xiangmin Fang,
Kuiling Zu,
Zhijun Luo,
Faxing Shen,
Fusheng Chen
Affiliations
Zhiming Xia
Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Kaitao Liao
Jiangxi Key Laboratory of Watershed Soil and Water Conservation, Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Liping Guo
Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Bin Wang
NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
Hongsheng Huang
College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China
Xiulong Chen
College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China
Xiangmin Fang
Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Kuiling Zu
Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Zhijun Luo
College of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, China
Faxing Shen
Jiangxi Key Laboratory of Watershed Soil and Water Conservation, Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China
Fusheng Chen
Key Laboratory of National Forestry and Grassland Administration on Forest Ecosystem Protection and Restoration of Poyang Lake Watershed, College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
Vegetation is a fundamental component of terrestrial ecosystems, and accurately assessing the effects of seasonal climate variations, extreme weather events, and land use changes on vegetation dynamics is crucial. The Ganjiang River Basin (GRB), a key region for water conservation and recharge in southeastern China, has experienced significant land use changes and variable climate in the past. However, comprehensive evaluations of how these changes have impacted vegetation remain limited. To address this gap, we used machine learning models (random forest and XGBoost) to assess the impact of seasonal and extreme climate variables, land cover, topography, soil properties, atmospheric CO2, and night-time light intensity on vegetation dynamics. We found that the annual mean NDVI showed a slight increase from 1990 to 1999 but has decreased significantly over the last 8 years. XGBoost was better than the RF model in simulating the NDVI when using all five types of data source (R2 = 0.85; RMSE = 0.04). The most critical factors influencing the NDVI were forest and cropland ratio, followed by soil organic carbon content, elevation, cation exchange capacity, night-time light intensity, and CO2 concentration. Spring minimum temperature was the most important seasonal climate variable. Both linear and nonlinear relationships were identified between these variables and the NDVI, with most variables exhibiting threshold effects. These findings underscore the need to develop and implement effective land management strategies to enhance vegetation health and promote ecological balance in the region.
