Combing Sentinel-1 and Sentinel-2 image time series for invasive Spartina alterniflora mapping on Google Earth Engine: a case study in Zhangjiang Estuary,Journal of Applied Remote Sensing

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Combing Sentinel-1 and Sentinel-2 image time series for invasive Spartina alterniflora mapping on Google Earth Engine: a case study in Zhangjiang Estuary
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2020-10-01 , DOI: 10.1117/1.jrs.14.044504
Di Dong, Chao Wang, Jinhui Yan, Qingyou He, Jisheng Zeng, Zheng Wei

As one of the most threatening invasive alien species to mangroves in China, Spartina alterniflora (S. alterniflora) has broadly existed along the Chinese tropical and subtropical coasts. Monitoring S. alterniflora with remote sensing is urgent and requisite for scientific invasive plant control and management. However, given the spectral similarity between S. alterniflora and other wetland types, such as mud covered by algae and the optical image coverage gaps due to cloud and tidal inundation in coastal areas, accurate and timely mapping of S. alterniflora is challenging. Using the extended Jeffries–Matusita distance (JBh), we first explored the best time window for detecting S. alterniflora with satellite data in Zhangjiang Estuary, Fujian, China. Then we presented a hierarchical classification framework to alleviate the spectral confusion problem, combining cost-free Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral image time series on the Google Earth Engine platform. Specifically, we integrated the inundation frequency map derived from the SAR time series, elevation, and slope criteria to calculate the potential areas of S. alterniflora and mangroves, then used the random-forest classification algorithm to identify S. alterniflora, and finally refined the classified map with a yearlong water mask. The optimal time windows of one month, two months, and three months identified by JBh were January, November and January, and November, January, and August, respectively; we got the high classification accuracies with corresponding overall accuracies of 99.35%, 99.63%, and 99.63%, respectively. The results suggested classification accuracy could be improved with a wider temporal window, but would saturate with 3-month imagery. The generated 10-m mangrove and S. alterniflora maps of 2017 and 2018 clearly showed the relatively stable spatial pattern of mangroves and the rapid expansion of S. alterniflora. The thriving S. alterniflora in Zhangjiang Estuary suggests the necessity of high-frequency and large-scale monitoring of invasive species along the coastal estuaries of China.

更新日期：2020-10-20

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