Forest Fire Severity Level Using dNBR Spectral Index

• 出版年: REVUE INTERNATIONALE DE GEOMATIQUE
• 主要な著者: Ghazali, Nur Nabihah; Saraf, Noraain Mohamed; Rasam, Abdul Rauf Abdul; Othman, Ainon Nisa; Salleh, Siti Aekbal; Saad, Nurhafiza Md
• フォーマット: 論文
• 言語: English
• 出版事項: TECH SCIENCE PRESS 2025
• 主題: Remote Sensing
• オンライン･アクセス: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001440739600001

Forest fires are contributing significantly to the acceleration of deforestation. Monitoring and mapping these fires are crucial, and remote sensing technology has proven effective for this purpose. This research employs remote sensing methods to evaluate the severity of a forest fire in Kampung Balai Besar, Dungun. The incident, covering a 23-hectare area, occurred on 15 June 2021. Initial data processing utilized Sentinel-2 satellite images from 14 June 2021 (pre-fire) and 19 June 2021 (post-fire). The extent and severity of the fire were assessed using the Normalized Burn Ratio (NBR) index derived from satellite images. Different levels of burn severity were classified based on the extracted NBR, and classes of the calculated index value were generated accordingly. The severity level and intensity of the burn were analyzed using the Difference Normalized Burn Ratio (dNBR) index. This spectral index aided in assessing the severity of the forest fire in Kampung Balai Besar. The findings revealed that 13.9% of the area experienced low severity burns, 47.2% had moderate to high severity burns, and 38.9% exhibited high severity burns. In summary, areas with moderate to high and high severity burns showed more significant damage, while unburned areas displayed higher vegetation productivity. The study successfully identified the burned area in Kampung Balai Besar, providing valuable information for early decision-making and effective forest management planning.