Post-Disaster Recovery Assessment of Mangrove Forests in Leyte Island, Philippines using Sentinel-2 Imagery

Published: 25 October 2023| Version 1 | DOI: 10.17632/w35ktjjzvn.1
Allen Glen Gil


The research investigated the synergy of different vegetation indices, biophysical variables, and landscape metrics in assessing the post-disaster recovery of the mangrove forests in Maasin City and Matalom in Leyte Island, Philippines after Super Typhoon Odette (International Name: Rai). These data show the monthly mean vegetation indices, monthly mean biophysical variables, and four-month landscape metrics values in the study area from 2019 to 2022, which spans three years before and one year after Super Typhoon Odette hit the area. The data were gathered using Google Earth Engine, SNAP Biophysical Processor, and QGIS Landscape Ecology Statistics Plug-In. Time series analysis was used to interpret the data, which includes the Theil-Sen Estimator and Mann-Kendall Test. Notable finding includes decline across all post-disaster parameters which reveal substantial damage to the functional, structural, and landscape configuration characteristics of the mangrove forest. Contrasting recovery and resiliency profiles were observed between vegetation indices and biophysical variables, which indicate that previous post-disaster studies that only employed NDVI may have reported underestimd recovery values.


Steps to reproduce

The vegetation indices (NDVI, NDII, and EVI) were computed via Google Earth Engine. The biophysical variables (FaPAR, FCOVER, LAI, Cab, and Cw) were computed using the SNAP Biophysical Processor. And the landscape metrics (CA, NP, AREA_MN, PLADJ, ED, PD, SHAPE_MN), were computed using supervised land cover classification using the Random Forest classifier in Google Earth Engine and then processed using the QGIS Landscape Ecology Statistics Plug-In.


University of the Philippines Los Banos


Environmental Science, Remote Sensing


Department of Science and Technology, Republic of the Philippines