IMPACT ASSESSMENT OF 2023 CYCLONE MOCHA ON VEGETATION AND FLOOD USING REMOTE SENSING IMAGE ANALYSIS

Cyclone Mocha (2023), a Category 5 tropical cyclone, caused significant damage in Myanmar, particularly in Sittwe, the capital of Rakhine State. Despite its strong intensity and the death toll of at least 500 people, the event has not been fully investigated due to limited data availability, especially in remote rural areas. To address this information gap, remote sensing techniques using optical satellite imagery and synthetic aperture radar (SAR) imagery were employed to assess Mocha’s impact on vegetation and inundation. Vegetation loss was analyzed using eVIIRS Global NDVI datasets, based on 10-day composite images of pre-, during-, and post-cyclone periods. The results revealed a small decrease in vegetation cover across over half of the study area, indicating a modest decline rather than complete destruction. In contrast, an increase in vegetation was also observed after the cyclone in areas away from the radius of maximum wind of the cyclone. SAR analysis suggested storm surge as the predominant factor contributing to the subsequent inundation. However, data from an inland town suggested the potential influence of riverine flooding and heavy precipitation as additional contributing factors. Using the CMEMS global model, the peak water level during the cyclone was estimated at 1.6 m at the closest point to the landfall location, marking the highest recorded level in the past 30 years. Furthermore, a local news photo showed visible inundation, likely caused by storm surge, reaching at least 0.5 m in downtown Sittwe.

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Journal
Journal of JSCE
Published
2025-01-01
DOI
https://doi.org/10.2208/journalofjsce.25-18044
Primary Topic
Flood Risk Assessment and Management
Type
article
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article

IMPACT ASSESSMENT OF 2023 CYCLONE MOCHA ON VEGETATION AND FLOOD USING REMOTE SENSING IMAGE ANALYSIS

Hiroshi Takagi, Md. Rezuanul Islam, Htut NAING THWIN
Journal of JSCE
Flood Risk Assessment and Management
article

IMPACT ASSESSMENT OF 2023 CYCLONE MOCHA ON VEGETATION AND FLOOD USING REMOTE SENSING IMAGE ANALYSIS

Hiroshi Takagi, Md. Rezuanul Islam, Htut NAING THWIN
article en

Abstract

Cyclone Mocha (2023), a Category 5 tropical cyclone, caused significant damage in Myanmar, particularly in Sittwe, the capital of Rakhine State. Despite its strong intensity and the death toll of at least 500 people, the event has not been fully investigated due to limited data availability, especially in remote rural areas. To address this information gap, remote sensing techniques using optical satellite imagery and synthetic aperture radar (SAR) imagery were employed to assess Mocha’s impact on vegetation and inundation. Vegetation loss was analyzed using eVIIRS Global NDVI datasets, based on 10-day composite images of pre-, during-, and post-cyclone periods. The results revealed a small decrease in vegetation cover across over half of the study area, indicating a modest decline rather than complete destruction. In contrast, an increase in vegetation was also observed after the cyclone in areas away from the radius of maximum wind of the cyclone. SAR analysis suggested storm surge as the predominant factor contributing to the subsequent inundation. However, data from an inland town suggested the potential influence of riverine flooding and heavy precipitation as additional contributing factors. Using the CMEMS global model, the peak water level during the cyclone was estimated at 1.6 m at the closest point to the landfall location, marking the highest recorded level in the past 30 years. Furthermore, a local news photo showed visible inundation, likely caused by storm surge, reaching at least 0.5 m in downtown Sittwe.

Journal of JSCEVol. 13(2)
Shanghai Institute for Science of Science (CN), Institute of Science Tokyo (JP), The University of Tokyo (JP)
Openalex Percentile: Top 71%
Flood Risk Assessment and Management
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IMPACT ASSESSMENT OF 2023 CYCLONE MOCHA ON VEGETATION AND FLOOD USING REMOTE SENSING IMAGE ANALYSIS — Hiroshi Takagi, Md. Rezuanul Islam, et al. · Journal of JSCE (2025) | TGRS Research Map | TGRS