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Desert shrubs are the dominant vegetation type in arid deserts and serve as crucial elements in sand retention, biodiversity maintenance, and carbon sequestration. However, due to their patchy and scattered distributions and spectral resemblance to herbaceous plants, desert shrub mapping relies on high-resolution imagery, which is less accessible for large-scale mapping. Here, a set of desert shrub mapping indices for Sentinel-2 (DSMIS) and for universal medium-resolution imagery (DSMIL) is developed to distinguish desert shrubs with dense vimen canopies. The index exploits the canopy structure and spectral characteristics, which have a sparse and multilayered canopy and a high proportion of desiccated branches, resulting in a consistently low reflectance in the red-edge to near-infrared range. The effectiveness of DSMI indices was examined in Ordos, China. In the experiment, an optimal threshold of 10.3 was obtained via DSMIs with Sentinel-2, which achieved an overall accuracy of 91.6% and identified a minimum desert shrub coverage of 0.23. In comparison, a threshold of 9.7 was obtained by DSMIL with Landsat-8, achieving an accuracy of 90.1% and identifying a minimum coverage of 0.17. The performance of DSMI was superior to that of the commonly used random forest, and this index could further improve classification as a complement to machine learning methods. The late stage of the nongrowing season was identified as the optimal period for desert shrub mapping with the index. DSMI also performed well at two test sites with diverse desert shrub dominant species and growing conditions. This study provides a novel index for desert shrub mapping and a practical tool for monitoring desertification in arid desert regions. It also offers a new perspective for historical dynamic studies of desert shrubs and other land cover types where only optical data are available.
Fu et al. (Fri,) studied this question.