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Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 95 - 117 Titre : Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping Type de document : Article/Communication Auteurs : Alvin B. Baloloy, Auteur ; Ariel C. Blanco, Auteur ; Raymund Rhommel StaAna, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 95 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spectrale [Termes IGN] Asie du sud-est [Termes IGN] carte de la végétation [Termes IGN] espèce exotique envahissante [Termes IGN] image captée par drone [Termes IGN] image Landsat-8 [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] mangrove [Termes IGN] orthophotographie [Termes IGN] Philippines [Termes IGN] surveillance du littoral Résumé : (auteur) 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 application of MVI were tested to four additional mangrove forests in Southeast Asia: Thailand, Vietnam, Indonesia and Cambodia; and to selected mangroves forests in South America, Africa and Australia. Numéro de notice : A2020-354 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.001 Date de publication en ligne : 11/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95240 [article]

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