4.3 Article

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

JOURNAL OF APPLIED REMOTE SENSING
Volume 14, Issue 4, Pages -

Publisher

SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JRS.14.044504

Keywords

invasive alien species; multisensor; random forest; Spartina alterniflora; time-series data

Funding

  1. Natural Science Foundation of Guangdong Province of China [2018A030310032]
  2. Open fund of the Key Laboratory for National Geography State Monitoring (Ministry of Natural Resources of the People's Republic of China) [2017NGCM08]
  3. Guangdong Key Lab of Ocean Remote Sensing (South China Sea Institute of Oceanology Chinese Academy of Sciences) [2017B030301005LORS1806]
  4. National Natural Science Foundation of China (NSFC) [41876205, 41906026]
  5. Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) [GML2019ZD0302]

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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 (J(Bh)), 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 J(Bh) 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. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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