The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 221–226, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-221-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 221–226, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-221-2021

  18 Nov 2021

18 Nov 2021

ASHFALL DISPERSAL MAPPING OF THE 2020 TAAL VOLCANO ERUPTION USING DIWATA-2 IMAGERY FOR DISASTER ASSESSMENT

E. J. G. Merin1, A. L. F. Yute1, C. J. S. Sarmiento1,2, and E. E. Elazagui1,2 E. J. G. Merin et al.
  • 1Department of Geodetic Engineering, College of Engineering, University of the Philippines Diliman, Philippines
  • 2Training Center for Applied Geodesy and Photogrammetry, University of the Philippines Diliman, Philippines

Keywords: Ash dispersal, Disaster Assessment, Taal volcano, Diwata-2, Remote Sensing, Support Vector Machine

Abstract. Natural disasters incur many fatalities and economic losses for vulnerable and developing countries such as the Philippines. It is crucial that during calamities, on-ground surveillance is supplemented by low-cost and time-efficient methods such as satellite remote sensing. Diwata-2 is a Philippine microsatellite specifically equipped for disaster assessment. In this study, the capabilities of this satellite in ashfall detection were explored by closely examining the case of the Taal volcano eruption on January 12, 2020. Satellite images covering parts of CALABARZON and Metropolitan Manila before and after the phreatomagmatic eruption were compared. The presence and extent of heavy ash over the study area were identified after the image classification using the Support Vector Machine (SVM) algorithm. A decrease in vegetation cover and built-up areas was also observed. Upon validation, an overall accuracy of 91.4562 and Kappa coefficient of 0.8833 were achieved for the post-eruption ashfall extent map, exhibiting the potential of Diwata-2 imagery in monitoring volcanic eruptions and similar phenomena.