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Precise vegetation restoration in arid regions based on potential natural vegetation and potential normalized difference vegetation index
Authors:Jia Qu  Qi Liu  Dongwei Gui  Xinlong Feng  Haolin Wang  Jianping Zhao  Guangyan Wang  Guanghui Wei
Institution:1. College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046 Xinjiang, 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 Xinjiang, China;2. 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 Xinjiang, China;3. College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046 Xinjiang, China;4. Xinjiang Tarim River Basin Management Bureau, Korla, 841000 Xinjiang, China

Abstract: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.
Keywords:arid region  ecosystem deterioration  machine learning  potential natural vegetation  potential normalized difference vegetation index  reforestation
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