Lv Zhentao, Li Shengyu, Xu Xinwen, Lei Jiaqiang, Peng Zhongmin
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, Xinjiang, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
J Environ Manage. 2025 Feb;374:124089. doi: 10.1016/j.jenvman.2025.124089. Epub 2025 Jan 9.
Vegetation restoration potential (VRP) assessment is an important aspect and foundation of ecological restoration projects. Neglecting the carrying capacity of the natural environment in the formulation and implementation of ecological restoration projects often leads to diminished effectiveness or even environmental damage. Existing models for VRP either overly rely on empirical knowledge, resulting in low efficiency and reproducibility, or fail to consider the nonlinear relationship between the natural environment and vegetation cover, leading to low accuracy in assessment results. Building upon existing models, this study proposes a new Vegetation Restoration Potential Mapping (VRPM) model based on a dual-variable discretization method for habitat similarity division and machine learning. Focused on Central Asia as the research area, the study evaluates the vegetation restoration potential of the region and validates the model. The results demonstrate that this model efficiently produces high-resolution and high-precision vegetation restoration potential maps. The average VRP in Central Asia is relatively low, around 36%, with most areas already having vegetation cover close to or reaching their restoration potential The regions with a higher degree of unrealized vegetation restoration potential (VRPU) are mainly distributed near human settlements, while VRPU is negative in some areas around the desert-oasis boundaries and artificial structures in the desert. The findings of this research demonstrate that the model can provide a basis for planning and implementing ecological restoration projects, thereby aiding in the health and sustainable development of ecosystems in arid regions.
植被恢复潜力（VRP）评估是生态恢复项目的重要方面和基础。在生态恢复项目的制定和实施过程中忽视自然环境的承载能力，往往会导致效果不佳甚至环境破坏。现有的VRP模型要么过度依赖经验知识，导致效率低下和可重复性差，要么未能考虑自然环境与植被覆盖之间的非线性关系，导致评估结果准确性低。本研究在现有模型的基础上，提出了一种基于双变量离散化方法进行栖息地相似性划分和机器学习的新型植被恢复潜力制图（VRPM）模型。以中亚为研究区域，对该地区的植被恢复潜力进行评估并验证模型。结果表明，该模型能够高效生成高分辨率和高精度的植被恢复潜力图。中亚地区的平均VRP相对较低，约为36%，大部分地区的植被覆盖已接近或达到其恢复潜力。未实现植被恢复潜力（VRPU）程度较高的区域主要分布在人类定居点附近，而在沙漠绿洲边界和沙漠中的人工建筑周围的一些地区，VRPU为负。本研究结果表明，该模型可为生态恢复项目的规划和实施提供依据，从而有助于干旱地区生态系统的健康和可持续发展。
