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High Resolution Fuel Type Mapping through Satellite Imagery and Neural Networks for Wildfire Simulations: A Case Study in Spain †

Marcos López-De-Castro
Diego Prieto-Herráez
Maria Isabel Asensio-Sevilla
2,3 and
Gianni Pagnini
BCAM-Basque Center for Applied Mathematics, Alameda de Mazarredo14, 48009 Bilbao, Spain
Applied Mathematics Department, University of Salamanca, Casas del Parque 2, 37008 Salamanca, Spain
Fundamental Physics and Mathematics University Institute, University of Salamanca, Casas del Parque 1, 37008 Salamanca, Spain
Ikerbasque-Basque Foundation for Science, Plaza Euskadi 5, 48009 Bilbao, Spain
Author to whom correspondence should be addressed.
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Environ. Sci. Proc. 2022, 17(1), 28;
Published: 9 August 2022
(This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk)


An important limitation in the simulation of forest fires is the correct characterisation of the surface vegetation documented in land cover maps. Unfortunately, these maps are not always available, or there is a lack of accuracy due to the dilated updating periods. These limitations can result in less-accurate predictions when wildfire models are applied to real-world situations employing information from these maps. New remote sensing technologies can provide up-to-date information on the state of the forest surfaces. On the other hand, in the last decade, we have also seen how artificial intelligence algorithms can efficiently process information to solve many different types of problems. Therefore, in this work we propose a complete procedure for fuel type mapping using satellite imagery and artificial deep neural networks. Specifically, our work is based on pixel-based processing cells, so the prediction of the fuel type is carried out by classifying isolated pixels, opening the door to generating high-resolution fuel-type maps. To test our technological solution, we studied an area located in Castile and León, a central Spanish region. The spectral information employed were collected from ETM+ sensor onboard Landsat 7 spacecraft and from ASTER sensor onboard Terra spacecraft. In addition, the classifier is also assisted with information about mean surface temperature and orography collected from MODIS device, and with several spectral indexes computed to enhance the spectral characteristics of the imagery. We have carried out classification of the surface vegetation for different fuel types, according to the Rothermel classification criteria adapted to the vegetation of the Iberian Peninsula. Results show an accuracy near 78%, improving some of the results reached in previous studies and demonstrating the robustness of our procedure.

Author Contributions

Conceptualization, M.L.-D.-C. and D.P.-H.; methodology, M.L.-D.-C.; software, M.L.-D.-C.; validation, M.L.-D.-C.; formal analysis, M.L.-D.-C.; investigation, M.L.-D.-C.; resources, D.P.-H., M.I.A.-S. and G.P.; data curation, M.L.-D.-C.; writing—original draft preparation, M.L.-D.-C.; writing—review and editing, D.P.-H., M.I.A.-S. and G.P.; visualization, M.L.-D.-C.; supervision, G.P, M.I.A.-S. and G.P.; project administration, M.I.A.-S. and G.P.; funding acquisition, M.I.A.-S. and G.P. All authors have read and agreed to the published version of the manuscript.


This research has been supported by the Basque Government through the BERC 2022–2025 program; by the Spanish Ministry of Economy and Competitiveness (MINECO) through the BCAM Severo Ochoa excellence accreditation SEV-2017-0718, and through the project PID2019-107685RB-I00; by the European Regional Development Fund (ERDF) and the Department of Education of the regional government, the Junta of Castilla y León, (Grant contract SA089P20); and by the European Union’s Horizon 2020—Research and Innovation Framework Program under Grant agreement ID 101036926. The Authors are also grateful to the anonymous referees for the useful remarks that improved the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The scripts developed throughout this work can be found here: (accessed on 8 August 2022).

Conflicts of Interest

The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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MDPI and ACS Style

López-De-Castro, M.; Prieto-Herráez, D.; Asensio-Sevilla, M.I.; Pagnini, G. High Resolution Fuel Type Mapping through Satellite Imagery and Neural Networks for Wildfire Simulations: A Case Study in Spain. Environ. Sci. Proc. 2022, 17, 28.

AMA Style

López-De-Castro M, Prieto-Herráez D, Asensio-Sevilla MI, Pagnini G. High Resolution Fuel Type Mapping through Satellite Imagery and Neural Networks for Wildfire Simulations: A Case Study in Spain. Environmental Sciences Proceedings. 2022; 17(1):28.

Chicago/Turabian Style

López-De-Castro, Marcos, Diego Prieto-Herráez, Maria Isabel Asensio-Sevilla, and Gianni Pagnini. 2022. "High Resolution Fuel Type Mapping through Satellite Imagery and Neural Networks for Wildfire Simulations: A Case Study in Spain" Environmental Sciences Proceedings 17, no. 1: 28.

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