Vegetation coverage precisely extracting and driving factors analysis in drylands

Abstract

Fractional Vegetation Coverage (FVC) is an essential indicator that captures variations in vegetation and documents the impacts of climate change and human activity for environmental assessment. However, conventional methods encounter challenges in accurately extracting fine-scale FVC in drylands due to the vegetation distribution being very heterogeneous in space with patches and inter-patches. Using the lower Tarim River Basin as a typical study case, we investigated three deep convolutional neural networks—Unet, Pspnet, and Deeplabv3 + —to generate high-precision FVC in drylands with high-resolution (0.8 m) remote sensing images. Among these models, the Unet model performed better, with an accuracy of 93.38%, while the accuracy of Pspnet and Deeplabv3+ was 88.14% and 88.91%, respectively. Comparison with the FVC derived from normalized difference vegetation index (NDVI), and land use/land cover data from ESRI and ESA indicated that the FVC map produced by Unet was more consistent with on-site field observations. Delving into drivers influencing dryland FVC, we found that groundwater depth plays a pivotal role compared to topographical and climatic variables. Specifically, when the groundwater depth exceeds −3 m, the probability of occurring high FVC is reduced to 50%. This study innovatively extracted the FVC of drylands with high vegetation spatial heterogeneity, which better solves the insufficient accuracy of the existing dataset, serves as a valuable reference for monitoring vegetation change, and facilitates more precise quantification of carbon storage.