Year 2020, Volume 1, Issue 2, 72 - 77, 15.12.2020

Suhrabuddin NASERY Kaan KALKAN

Abstract

Burn area detection and burn severity assessment using Sentinel 2 MSI data: The case of Karabağlar district, İzmir/Turkey

Abstract

Forest fires are serious environmental problems for ecosystem which destroys huge amount of forests every year across the world. Detecting burned areas is not the only important task, it is also very important to distinguish severity degrees of soil suffered for post-fire land management and vegetation regeneration. Remote sensing presents accurate and efficient methods for mapping burned areas and assessing burn severity levels. In this study we detected forest burn areas and assessed burn severity levels using Remote Sensing techniques and Sentinel 2 satellite data products in Karabağlar, Menderes and Seferihisar districts of İzmir, Turkey. A recent big forest fire occurred on 18 August 2019 is assessed in this study, which burned down about 500 hectares of the forest. Burned areas are detected using Normalized Burn Ratio (NBR) and Soil Adjusted Vegetation Index (SAVI) indices and burn area severity analysis is performed using Differenced Normalized Burn Ratio (dNBR) and Differenced Soil Adjusted Vegetation Index (dSAVI) indices. The results of dNBR index show that a total of 6909.708-hectare area is burned during forest fire while 11184.502-hectare is unburned. Areas with different levels of burn severity were detected: 9.3 % Low, 11.1% Moderate-low, 8.5% Moderate-high and 9.3% High. Furthermore, based on the results of dSAVI analysis, 6699.554-hectare area is burned and 11394.656-hectare is unburned; the following different levels of burn severity were detected in the area: 9.4 % Low, 6.4% Moderate-low, 8.7% Moderate-high and 12.5% High.

Keywords

Forest fire, Burn severity, NBR, dNBR, SAVI, Sentinel 2

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