The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Nov 2021)

MONITORING POST-DISASTER MANGROVE FOREST RECOVERIES IN LAWAAN-BALANGIGA, EASTERN SAMAR USING TIME SERIES ANALYSIS OF MOISTURE AND VEGETATION INDICES

  • K. V. Ticman,
  • S. G. Salmo III,
  • K. E. Cabello,
  • M. Q. Germentil,
  • D. M. Burgos,
  • A. C. Blanco,
  • A. C. Blanco

DOI
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-295-2021
Journal volume & issue
Vol. XLVI-4-W6-2021
pp. 295 – 301

Abstract

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The mangrove forests of Lawaan-Balangiga in Eastern Samar lost significant cover due to the Typhoon Haiyan that struck the region in 2013. The mangroves in the area have since shown signs of recovery in terms of growth and spatial coverage, but these widely varied with locations. This study aims to further examine the status of recovery of mangroves across different locations by analysing the time series trends of selected vegetation and moisture indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), and Normalized Difference Moisture Index (NDMI). These indices were extracted from Landsat 8 surface reflectance images, spanning 2014 to 2020, using Google Earth Engine (GEE). The time series analyses showed similar NDVI, MSAVI and NDMI values and trends after the 2013 typhoon event. The trend slopes also indicated high correlation (0.91 – 1.00) between and among the indices, with NDVI having the highest correlation with MSAVI (∼1.00). The study was able to corroborate the previous study on mangroves in Lawaan-Balangiga, by presenting positive trend results in the identified recovered areas. These trends, however, would still have to be validated by collecting and comparing biophysical parameters in the field. The next step of the research would be to identify the factors that contribute to the varying rates of recovery in the areas and to evaluate how this can affect the carbon sequestration rates of recovering mangroves.