Abstract
Human activity and climate change are degrading the environmentally fragile Loess Plateau in dry and semiarid regions. Land deterioration threatens human and ecological existence. To prevent additional land degradation and ensure the ecological development and quality of arable land in the region, China launched “Grain for Green” in the late 1990s. This effort greatly boosted Loess Plateau vegetation. However, land degradation is complex, and so we must also examine natural and social variables to prevent additional degradation. Thus, this study presents a comprehensive index system to quantify land degradation on the Loess Plateau and uses machine learning to anticipate high-risk locations. The project improved land degradation, and the spatial distribution of degradation risk is high in the northern and low in the eastern and southern regions of the Plateau. Gross Domestic Product and population density are the main drivers of land degradation. Industrialization and urbanization have raised the risk of land degradation, which now accounts for 1%–2% of the area. This study emphasizes sustainable land management in the Loess Plateau, a critical area for sustainable development in China. The integrated assessment indicator system and random forest modeling machine learning help grasp the current status and future preventive measures. The outcome of this study advances the Loess Plateau land degradation research and sustainable land management. The research findings possess significant scientific reference value in terms of mitigating and managing land degradation in environmentally vulnerable regions worldwide.
Original language | English |
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Pages (from-to) | 2409-2424 |
Number of pages | 16 |
Journal | Land Degradation and Development |
Volume | 35 |
Issue number | 7 |
DOIs | |
Publication status | Published - 30 Apr 2024 |
Keywords
- Grain for Green Project
- Loess Plateau
- NDVI
- land degradation
- random forest
ASJC Scopus subject areas
- Environmental Chemistry
- Development
- General Environmental Science
- Soil Science