Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping

• Published in: ISPRS journal of photogrammetry and remote sensing 2020-08, Vol.166, p.95-117
• Main Authors: Baloloy, Alvin B., Blanco, Ariel C., Sta. Ana, Raymund Rhommel C., Nadaoka, Kazuo
• Format: Article
• Language: English
• Subjects: Landsat; Mangrove extent; Mangrove index; Philippines; Sentinel-2; Vegetation mapping
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2020.06.001

•A new Mangrove Vegetation Index (MVI) using green, NIR, and SWIR1 bands is presented.•MVI measures the greenness and moisture information, with an index accuracy of 92%.•MVI was strongly and positively correlated with Sentinel-2 biophysical products.•Two mapping automation tools, the IDL and GEE-based MVI Mapper were developed.•The 2019 Philippine mangrove extent was mapped using MVI. Advancement in Remote Sensing allows rapid mangrove mapping without the need for data-intensive methodologies, complex classifiers, and skill-dependent classification techniques. This study proposes a new index, the Mangrove Vegetation Index (MVI), to rapidly and accurately map mangroves extent from remotely-sensed imageries. The MVI utilizes three Sentinel-2 bands green, Near Infrared (NIR) and Shortwave Infrared (SWIR) in the form |NIR-Green|/|SWIR-Green| to discriminate the distinct greenness and moisture of mangroves from terrestrial vegetation and other land cover. Spectral band analysis shows that the |NIR-Green| part of MVI captures the differences of greenness between mangrove forests and terrestrial vegetation. The |SWIR-Green| part of the index expresses the distinct moisture of mangroves without the need for additional intertidal data and water indices. The MVI value increases with the increasing probability of a pixel being classified as mangroves. Eleven mangrove forests in the Philippines and one mangrove park in Japan were then mapped using MVI. Accuracy assessment was done using field inventory data and high-resolution drone orthophotos. MVI have successfully separated the mangroves from other cover especially terrestrial vegetation, with an overall index accuracy of 92%. The MVI was applied to Landsat 8 images using the equivalent bands to test the universality of the index. Comparable MVI mangrove maps were produced between Sentinel-2 and Landsat images, with an optimal minimum threshold of 4.5 and 4.6, respectively. MVI can effectively highlight the greenness and moisture information in mangroves as reflected by its moderate to high correlation value (r = 0.63 and 0.84, α = 0.05) with the Sentinel-derived chlorophyll-a (Ca) and canopy water (Cw) biophysical products. This study developed and implemented two automated platforms: an offline IDL-based ‘MVI Mapper’ and an online Google Earth Engine-based MVI mapping interface. The MVI implemented in Google Earth Engine was used in generating the latest mangrove extent map of the Philippines. Additionally, the