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Vegetation and Soil Fire Damage Analysis Based on Species Distribution Modeling Trained with Multispectral Satellite Data

1
Electronic Technology Department, University of Valladolid, Paseo del Cauce, 59, 47011 Valladolid, Spain
2
Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agricultural and Food Research and Technology, Spain
3
Department of Geography, University of California, Santa Barbara, CA 93106, USA
4
Agrarian Science and Engineering Department, University of León, Av. Astorga s/n. 24400 Ponferrada, Spain
5
Area of Ecology, Faculty of Biological and Environmental Sciences, University of León, 24071 León, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1832; https://doi.org/10.3390/rs11151832
Received: 13 July 2019 / Revised: 30 July 2019 / Accepted: 2 August 2019 / Published: 6 August 2019
(This article belongs to the Section Forest Remote Sensing)
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Abstract

Forest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers. View Full-Text
Keywords: soil burn severity; vegetation burn severity; burned area; composite burn index (CBI); land surface temperature (LST); sentinel-2 soil burn severity; vegetation burn severity; burned area; composite burn index (CBI); land surface temperature (LST); sentinel-2
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Quintano, C.; Fernández-Manso, A.; Calvo, L.; Roberts, D.A. Vegetation and Soil Fire Damage Analysis Based on Species Distribution Modeling Trained with Multispectral Satellite Data. Remote Sens. 2019, 11, 1832.

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