Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, 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 so...
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2025-01-01
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| author | Zhiming Xia Kaitao Liao Liping Guo Bin Wang Hongsheng Huang Xiulong Chen Xiangmin Fang Kuiling Zu Zhijun Luo Faxing Shen Fusheng Chen |
| author_facet | Zhiming Xia Kaitao Liao Liping Guo Bin Wang Hongsheng Huang Xiulong Chen Xiangmin Fang Kuiling Zu Zhijun Luo Faxing Shen Fusheng Chen |
| author_sort | Zhiming Xia |
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| description | 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 CO<sub>2</sub>, 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 (R<sup>2</sup> = 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 CO<sub>2</sub> 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. |
| format | Article |
| id | doaj-art-02342c93d3a94d4997578b3cb7103a35 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-02342c93d3a94d4997578b3cb7103a352025-01-24T13:37:48ZengMDPI AGLand2073-445X2025-01-011417610.3390/land14010076Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, ChinaZhiming Xia0Kaitao Liao1Liping Guo2Bin Wang3Hongsheng Huang4Xiulong Chen5Xiangmin Fang6Kuiling Zu7Zhijun Luo8Faxing Shen9Fusheng Chen10Key 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, ChinaJiangxi 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, ChinaKey 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, ChinaNSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, AustraliaCollege of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, ChinaCollege of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, ChinaKey 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, ChinaKey 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, ChinaCollege of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, ChinaJiangxi 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, ChinaKey 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, ChinaVegetation 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 CO<sub>2</sub>, 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 (R<sup>2</sup> = 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 CO<sub>2</sub> 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.https://www.mdpi.com/2073-445X/14/1/76NDVImachine learningdriving factorsclimate variableland coverGanjiang River Basin |
| spellingShingle | Zhiming Xia Kaitao Liao Liping Guo Bin Wang Hongsheng Huang Xiulong Chen Xiangmin Fang Kuiling Zu Zhijun Luo Faxing Shen Fusheng Chen Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China Land NDVI machine learning driving factors climate variable land cover Ganjiang River Basin |
| title | Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China |
| title_full | Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China |
| title_fullStr | Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China |
| title_full_unstemmed | Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China |
| title_short | Determining Dominant Factors of Vegetation Change with Machine Learning and Multisource Data in the Ganjiang River Basin, China |
| title_sort | determining dominant factors of vegetation change with machine learning and multisource data in the ganjiang river basin china |
| topic | NDVI machine learning driving factors climate variable land cover Ganjiang River Basin |
| url | https://www.mdpi.com/2073-445X/14/1/76 |
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