Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/25612

Title: Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV
Authors: Carvajal-Ramírez, Fernando
Marques da Silva, J.
Aguera-Vera, F.
Martínez-Carricondo, P.
Serrano, João
Moral, F.
Editors: MDPI
Keywords: Fire Severity
UAV
Multispectral Imagery
Issue Date: 26-Apr-2019
Publisher: MDPI
Citation: Carvajal-Ramírez, F., Marques da Silva, J.R., Agüera-Vega, F., Martínez-Carricondo, P. Serrano, J. Moral, F. J. (2019). Evaluation of fire severity indices based on pre- and 3 post-fire multispectral imagery sensed from UAV. Remote Sensing, 11, 993. ACEITE em 24ABR2019, Publicado em 26/04/201
Abstract: 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.
URI: http://hdl.handle.net/10174/25612
Type: article
Appears in Collections:ICAAM - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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