State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China; College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830017, China.
State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China; Cele National Station of Observation & Research for Desert Grassland Ecosystem in Xinjiang, Cele, 848300, China.
J Environ Manage. 2024 Sep;367:121934. doi: 10.1016/j.jenvman.2024.121934. Epub 2024 Jul 31.
Ecological restoration is imperative for controlling desertification. Potential natural vegetation (PNV), the theoretical vegetation succession state, can guides near-natural restoration. Although a rising transition from traditional statistical methods to advanced machine learning and deep learning is observed in PNV simulation, a comprehensive comparison of their performance is still unexplored. Therefore, we overview the performance of PNV mapping in terms of 12 commonly used methods with varying spatial scales and sample sizes. Our findings indicate that the methodology should be carefully selected due to the variation in performance of different model types, with Area Under the Curve (AUC) values ranging from 0.65 to 0.95 for models with sample sizes up to 80% of the total sample size. Specifically, semi-supervised learning performs best with small sample sizes (i.e., 10 to 200), while Random Forest, XGBoost, and artificial neural networks perform better with large sample sizes (i.e., over 500). Further, the performance of all models tends to improve significantly as the sample size increases and the grain size of the crystals becomes smaller. Take the downstream Tarim River Basin, a hyper-arid region undergoing ecological restoration, as a case study. We showed that its potential restored areas were overestimated by 2-3 fold as the spatial scale became coarser, revealing the caution needed while planning restoration projects at coarse resolution. These findings enhance the application of PNV in the design of restoration programs to prevent desertification.
生态恢复对于控制荒漠化至关重要。潜在自然植被（PNV）是理论上的植被演替状态，可以指导近自然恢复。尽管在 PNV 模拟中，从传统统计方法到先进的机器学习和深度学习的转变趋势正在上升，但它们的性能综合比较仍未得到探索。因此，我们从空间尺度和样本大小等方面，对 12 种常用方法的 PNV 制图性能进行了综述。我们的研究结果表明，由于不同模型类型的性能存在差异，因此应仔细选择方法，模型类型的 AUC 值范围从 0.65 到 0.95，样本大小为总样本量的 80%。具体而言，半监督学习在小样本量（即 10 到 200）下表现最佳，而随机森林、XGBoost 和人工神经网络在大样本量（即超过 500）下表现更好。此外，随着样本量的增加和晶粒度的减小，所有模型的性能都有显著提高。以正在进行生态恢复的超干旱地区塔里木河流域为例。结果表明，随着空间尺度的变粗，其潜在的恢复面积被高估了 2-3 倍，这表明在以粗分辨率规划恢复项目时需要谨慎。这些发现增强了 PNV 在设计恢复计划中的应用，以防止荒漠化。
