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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1139–1146, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1139-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1139–1146, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1139-2022
 
31 May 2022
31 May 2022

RAPID MAPPING AND ASSESSMENT OF DAMAGES DUE TO TYPHOON RAI USING SENTINEL-1 SYNTHETIC APERTURE RADAR DATA

S. F. Meneses III1,2 and A. C. Blanco1,2 S. F. Meneses III and A. C. Blanco
  • 1Philippine Space Agency, University of the Philippines Diliman, Quezon City, Philippines
  • 2Department of Geodetic Engineering, UP Diliman, Quezon City, Philippines

Keywords: Disaster Mapping, Disaster Response, Typhoon Odette, UNITAR, UNOSAT

Abstract. Typhoon Rai has recently affected central and southern parts of the Philippines. Based on the valuation of the country’s National Disaster Risk Reduction and Management Council (NDRRMC), it is estimated that the typhoon has damaged 1,700 buildings, 2 million houses, and 10 million hectares of agricultural land in the affected locations. Given the effects of the typhoon, in terms of the extent of the area where it has caused destruction, the tremendous economic losses due to its incurred damages, and to the number of people affected by it, it became necessary to create rapid damage assessment maps that could provide the needed geospatial information to emergency responders so they can prioritize areas of most concern. In this effort, Sentinel-1 synthetic aperture radar (SAR) imagery was used due to the data’s temporal resolution (i.e., pre- and post-disaster images are available), relative independence from atmospheric conditions (i.e., unaffected by cloud cover), and open-access availability (i.e., data can be readily downloaded after the typhoon). Complex coherence correlation from stacks of pre- and post-disaster SAR images were analyzed for change detection in order to create the rapid damage assessment maps. In order to validate the results, ancillary data (i.e., aerial photos, local reports, and UNITAR / UNOSAT damage tags and maps) were used to qualitatively and quantitatively assess the maps. Upon analysis, we found that there is good correspondence between the SAR-derived maps and the aerial photos/UNITAR maps and that the damage tags by UNITAR / UNOSAT would match the rapid damage assessment maps to up to 93% if the correlation threshold is set to 0.5 and if the damage classification is set to just two levels (i.e., “damaged” and “not damaged”). It is deemed that the resulting maps of this research will be useful in the on-going efforts to rehabilitate the affected areas of Typhoo Rai. Future work includes further ground validation efforts and use of other datasets and methods in deriving the rapid damage assessment maps.