Mapping mangrove in Dongzhaigang, China using Sentinel-2 imagery

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19 Citations (Scopus)


Mangroves not only protect the coastline from ocean waves and storms but also provide economical, ecological, and social functions for the human being. Despite the important role mangrove plays, mangrove has suffered great loss over the past decades. Mapping the mangrove extent is essential for its conservation. We examined the potential of original bands, spectral indices, including combined mangrove recognition index (CMRI) and normalized difference vegetation index (NDVI), and principal component analysis (PCA) of Sentinel-2 to map the mangrove of Dongzhaigang, China in June 2019. Random forest (RF) approach was adopted for the image classification. Results showed that the overall accuracy and kappa coefficient of the image classification can achieve 90.47% and 0.86. The area of mangrove of Dongzhaigang, China in June 2019 is 17.83  km2. The importance of each variable was calculated and among all the 15 variables, the CMRI ranked first, followed by NDVI, band 3 (green), band 2 (blue), and PCA1 (the first principal component) in turn. Based on the results, we can conclude that vegetation indices and PCA are essential for mangrove extent mapping since the variable importance of these features are relatively higher in the ranking of mean decrease Gini. Also band 3 (green band) provides important information for distinguishing mangrove from land and water class. Furthermore, combining vegetation indices, PCA, and RF model can accurately delineate the extent of mangrove.
Original languageEnglish
Article number014508
JournalJournal of Applied Remote Sensing
Issue number1
Publication statusPublished - 20 Jan 2020
Externally publishedYes


  • combined mangrove recognition index
  • image classification
  • mangrove
  • principal component analysis
  • random forest


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