Abstract
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 map** 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 map** 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.
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Acknowledgements
This research was jointly financially supported by the National Natural Science Foundation of China (Project Nos. 41671422), the National Key Research and Development Program of China (Project No. 2016YFC1402701), the Strategic Priority Research Program of Chinese Academy of Sciences (Project No. XDA20030302), the National Natural Science Foundation of China (Project Nos. 4151144012, 41661144030), the Innovation Project of LREIS (Project Nos. 088RA20CYA, 08R8A010YA), and the National Mountain Flood Disaster Investigation Project (Project No. SHZH-IWHR-57). Thanks to China Center of Resources Satellite Data and Application for providing the GF-1 data products.
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Liu, Q., Huang, C. & Li, H. Map** plant communities within quasi‐circular vegetation patches using tasseled cap brightness, greenness, and topsoil grain size index derived from GF-1 imagery. Earth Sci Inform 14, 975–984 (2021). https://doi.org/10.1007/s12145-021-00608-3
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DOI: https://doi.org/10.1007/s12145-021-00608-3