Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

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Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

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Title: Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity
Author: Quintano Pastor, Carmen;Fernández Manso, Alfonso;Fernández-Manso, Óscar
xmlui.dri2xhtml.METS-1.0.item-contributor: Escuela Superior y Tecnica de Ingenieria Agraria
xmlui.dri2xhtml.METS-1.0.item-area: Ingenieria Agroforestal
Abstract: Nowadays Earth observation satellites, in particular Landsat, provide a valuable help to forest managers in post-fire operations; being the base of post-fire damage maps that enable to analyze fire impacts and to develop vegetation recovery plans. Sentinel-2A MultiSpectral Instrument (MSI) records data in similar spectral wavelengths that Landsat 8 Operational Land Imager (OLI), and has higher spatial and temporal resolutions. This work compares two types of satellite-based maps for evaluating fire damage in a large wildfire (around 8000 ha) located in Sierra de Gata (central-western Spain) on 6–11 August 2015. 1) burn severity maps based exclusively on Landsat data; specifically, on differenced Normalized Burn Ratio (dNBR) and on its relative versions (Relative dNBR, RdNBR, and Relativized Burn Ratio, RBR) and 2) burn severity maps based on the same indexes but combining pre-fire data from Landsat 8 OLI with post-fire data from Sentinel-2A MSI data. Combination of both Landsat and Sentinel-2 data might reduce the time elapsed since forest fire to the availability of an initial fire damage map. Interpretation of ortho-photograph Pléiades 1 B data (1:10,000) provided us the ground reference data to measure the accuracy of both burn severity maps. Results showed that Landsat based burn severity maps presented an adequate assessment of the damage grade (κ statistic = 0.80) and its spatial distribution in wildfire emergency response. Further using both Landsat and Sentinel-2 MSI data the accuracy of burn severity maps, though slightly lower (κ statistic = 0.70) showed an adequate level for be used by forest managers
xmlui.dri2xhtml.METS-1.0.item-desfisica: 5 p.
xmlui.dri2xhtml.METS-1.0.item-peerreviewed: SI
Publisher: Elsevier
xmlui.dri2xhtml.METS-1.0.item-citation: International Journal of Applied Earth Observations and Geoinformation, 2017, vol. 57
Date: 2017-02-23
xmlui.dri2xhtml.METS-1.0.item-tipo: info:eu-repo/semantics/article
Subject: Ingeniería agrícola
Ingeniería forestal
xmlui.dri2xhtml.METS-1.0.item-palclave: Sentinel-2
Incendios forestales
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