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.
Floodplain hydro-geomorphology is the major factor for vegetation growth and biomass. Characterizing interaction between the vegetation and hydro-geomorphology is essential to better understanding river ecosystems in arid regions. In this study, considering the various hydro-geomorphological dynamics of floodplains, the study area was divided into four functional zones and spatialized lateral variations of the floodplain fluvial disturbance intensity, sediment erosion and deposition process, moisture dynamics and vegetation growth condition through Landsat and daily Moderate Resolution Imaging Spectroradiometer (MODIS) time series between 2010-2015. And analyzed the various interactions between floodplain vegetation and hydro-geomorphology in different floodplain lateral zones. Results shows that the highest vegetation NDVI values (>0.4) are distributed in the regularly static floodplain area where characterized regular flood inundation and has moderate moisture ability. Vegetation NDVI values in the frequency inundation, wet and highly dynamic floodplain area are mainly less than 0.2. Results indicate that different hydro-geomorphological processes dominated in floodplains, therefore plants and hydro-geomorphology interact each other in different ways from river to floodplain margins. Using remote sensing time series can be better characterizing the different interactions between floodplain vegetation and hydro-geomorphology. This study can help to better management of river ecosystems in the arid region floodplains.
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