LAUSR

Quantifying the structure and composition of quasi-circular vegetation patches (QVPs) is key in identifying ecosystem function, which will help create a cost-effective nature-based solution for restoring the degraded wetland ecosystem in the Yellow River Delta (YRD), China. However, research on mapping plant communities of QVPs using remotely sensed data has not been conducted. In this study, we found that the pan-sharpened GF-1 imagery acquired in May was suitable for mapping plant communities of QVPs. Guided by field survey data and finer spatial resolution remotely sensed data, we constructed a simple decision tree classifier using the tasseled cap brightness (TCB), greenness (TCG), and topsoil grain size index (TGSI) of the pan-sharpened GF-1 image acquired in May. The classification results showed that the combination of the TCB and TCG components could efficiently distinguish the vegetation from non-vegetation, and the use of the TGSI was able to capture the variations in plant communities within QVPs in the YRD, China. However, the influence of the acquisition season and mixed pixels of GF-1 imagery (especially small canopy T. chinensis in small QVPs) on classification accuracy still needs further investigation.