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Article

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

1
Centre of Technology and Systems/UNINOVA, School of Science and Technology–NOVA University of Lisbon, 2829-516 Caparica, Portugal
2
Forest Research Centre, School of Agriculture–University of Lisbon, 1349-017 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 909; https://doi.org/10.3390/rs12060909
Received: 3 February 2020 / Revised: 5 March 2020 / Accepted: 9 March 2020 / Published: 12 March 2020
The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations. View Full-Text
Keywords: remote sensing; fire break; object-based classification; change detection; wildfires; artificial neural networks; sentinel-2 remote sensing; fire break; object-based classification; change detection; wildfires; artificial neural networks; sentinel-2
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MDPI and ACS Style

Pereira-Pires, J.E.; Aubard, V.; Ribeiro, R.A.; Fonseca, J.M.; Silva, J.M.N.; Mora, A. Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network. Remote Sens. 2020, 12, 909. https://doi.org/10.3390/rs12060909

AMA Style

Pereira-Pires JE, Aubard V, Ribeiro RA, Fonseca JM, Silva JMN, Mora A. Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network. Remote Sensing. 2020; 12(6):909. https://doi.org/10.3390/rs12060909

Chicago/Turabian Style

Pereira-Pires, João E., Valentine Aubard, Rita A. Ribeiro, José M. Fonseca, João M. N. Silva, and André Mora. 2020. "Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network" Remote Sensing 12, no. 6: 909. https://doi.org/10.3390/rs12060909

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