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

João Pereira-Pires, Valentine Aubard, Rita A. Ribeiro, José M. Fonseca, João M. N. Silva, André Mora

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.

Original languageEnglish
JournalRemote Sensing
Volume12
Issue number6
DOIs
Publication statusPublished - 12 Mar 2020

Keywords

  • Artificial neural networks
  • Change detection
  • Fire break
  • Object-based classification
  • Remote sensing
  • Sentinel-2
  • Wildfires

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