基于潜在自然植被和潜在归一化植被指数的干旱区植被精准恢复,Restoration Ecology

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基于潜在自然植被和潜在归一化植被指数的干旱区植被精准恢复
Restoration Ecology ( IF 2.8 ) Pub Date : 2023-07-05 , DOI: 10.1111/rec.13967
Jia Qu _{1,

2} , Qi Liu ₂ , Dongwei Gui _{2,

3} , Xinlong Feng ₁ , Haolin Wang _{1,

2} , Jianping Zhao ₁ , Guangyan Wang ₄ , Guanghui Wei ₄

Affiliation

精确的植被恢复对于旱地至关重要，因为一些不适当的恢复尝试甚至加剧了造林地区的水资源短缺和退化。潜在自然植被（PNV）被广泛用于为恢复方案的适当位置和植被类型提供参考，而适当的恢复区域仍然未知。因此，我们提出了一种基于多种机器学习（ML）算法的PNV-潜在归一化植被指数（PNDVI）耦合框架，用于精确的旱地植被恢复。以总面积1182 km ²的塔里木河下游流域为例，定量估算了其目前适宜的恢复地点、面积和适宜的种植品种。结果表明，将 PNDVI 纳入 PNV 中并具有易于测量和可用的数据（例如温度和土壤特性）所开发的模型可以准确识别旱地恢复模式。在LTRB中，树木和草地的潜在适宜生境距离河岸较近，而灌木生境则距离球场较远，分别覆盖1.88、2.96和25.12 km ²。 LTRB 现有树木和草地的进一步扩张仍有巨大的土地潜力，现有土地的树木和草地分别占 2.56% 和 1.54%。这项研究的新颖之处在于结合 PNV 和 PNDVI，通过多种 ML 算法来量化和估计精确的恢复模式。该模型可用于评价旱地适宜造林地点、面积和植被类型，为精准植被恢复提供依据。

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Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index

Precise vegetation restoration is critical in drylands, as some inappropriate restoration attempts have even increased water scarcity and degradation in afforestation areas. Potential natural vegetation (PNV) is widely used to provide a reference for the appropriate location and vegetation type of restoration programs while the appropriate restored areas remain unknown. Therefore, we proposed a PNV–potential normalized difference vegetation index (PNDVI) coupling framework based on multiple machine learning (ML) algorithms for precise dryland vegetation restoration. Taking the lower Tarim River Basin (LTRB) with a total area of 1,182 km² as a case study, its present suitable restoration locations, area, and appropriate planting species were quantitatively estimated. The results showed that the model developed by incorporating PNDVI into PNV with easily measurable and available data such as temperature and soil properties can accurately identify dryland restoration patterns. In LTRB, the potentially suitable habitats of trees and grass are closer to the riverbank, while shrubby habitats are further away from the course, covering 1.88, 2.96, and 25.12 km², respectively. There is still enormous land potential for further expansion of the current trees and grass in the LTRB, with 2.56 and 1.54% of existing land supposed to be trees and grass, respectively. This study's novel aspect is combining PNV and PNDVI to quantify and estimate precise restoration patterns through multiple ML algorithms. The model developed here can be used to evaluate the suitable reforestation locations, area, and vegetation types in drylands and to provide a basis for precise vegetation restoration.

更新日期：2023-07-05

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