The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 235–242, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-235-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 235–242, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-235-2021

  18 Nov 2021

18 Nov 2021

LIMITATION ASSESSMENT AND WORKFLOW REFINEMENT OF THE MANGROVE VEGETATION INDEX (MVI)-BASED MAPPING METHODOLOGY USING SENTINEL-2 IMAGERY

M. P. Neri1, A. B. Baloloy1, and A. C. Blanco1,2 M. P. Neri et al.
  • 1Department of Geodetic Engineering, University of the Philippines, Diliman, 1101, Philippines
  • 2Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1101, Philippines

Keywords: mangroves, MVI, Sentinel-2, vegetation mapping, threshold

Abstract. The Mangrove Vegetation Index (MVI) was developed to map mangroves extent from remotely-sensed imageries accurately and quickly. MVI measures the probability of a pixel to be a ‘mangrove’ by extracting the greenness and moisture information from the green, NIR, and SWIR bands. The range of MVI values may vary depending on factors such as land cover classes, climatic conditions, or tidal conditions. Mapping the scope of mangrove sites involves setting a maximum and minimum MVI threshold to separate them from other land cover classes and vegetation. Although the MVI has a high index accuracy, its mapping performance is limited by some biophysical and environmental factors. Misclassification occurs in aquacultural areas, irrigated croplands, and sites with palm trees where mangrove and surrounding vegetation pixels have highly similar spectral signatures. There are scenes with complex environments, such as in aquaculture areas and along a network of rivers and streams, where an optimal threshold varies across the site, and setting a single MVI threshold may not yield excellent results. An automated threshold setting using the Otsu method was explored; however, the results were inaccurate due to a low intensity contrast between mangroves and other vegetation in the MVI raster layer. This study also looked into possible adjustments to improve the manual threshold setting workflow for a successful mapping of mangrove extent using MVI on Sentinel-2 imagery.