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Open AccessArticle

Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

1
Department of Engineering, Mediterranean Research Center of Economics and Sustainable Development (CIMEDES), University of Almería (Agrifood Campus of International Excellence, ceiA3). La Cañada de San Urbano, s/n. 04120 Almería, Spain
2
Rural Engineering Department, University of Évora, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Apartado 94, 7002-554 Évora, Portugal
3
Agroinsider. PITE-R. Circular Norte, NERE Sala 12.10, 7005-841 Évora Portugal
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Peripheral Service of Research and Development based on drones, University of Almeria. La Cañada de San Urbano, s/n. 04120 Almería, Spain
5
Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura. Avda. de Elvas s/n, 06006 Badajoz, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 993; https://doi.org/10.3390/rs11090993
Received: 5 April 2019 / Revised: 23 April 2019 / Accepted: 24 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Drone Remote Sensing)
Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications. View Full-Text
Keywords: Fire Severity; UAV; Multispectral Imagery Fire Severity; UAV; Multispectral Imagery
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MDPI and ACS Style

Carvajal-Ramírez, F.; Marques da Silva, J.R.; Agüera-Vega, F.; Martínez-Carricondo, P.; Serrano, J.; Moral, F.J. Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV. Remote Sens. 2019, 11, 993.

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