Pixel-based and object-based change detection methods for assessing fuel break maintenance

Joao E. Pereira-Pires, Valentine Aubard, João M. N. Silva, Rita A. Ribeiro, José M. C. Pereira, José Manuel Fonseca, Manuel L. Campagnolo, André Mora

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


This last decade, large wildfires have increased in number, size and consequent damages in various countries worldwide. Since 2017, the large fire hazard is a major concern for Portugal. An important fuel break (FB) network is currently implemented in strategic areas by the Portuguese Institute of Nature and Forest Conservation (ICNF). The objective of reducing fuel loads on those thin strips is to reduce fire propagation and to improve firefighting conditions. The efficiency of FB depends on its periodic maintenance. The increasing quality and frequency of Earth Observation Satellite imagery nowadays allow the implementation of change detection methods to identify the occurrence of FB maintenance operations and help their necessary management. This article presents two approaches, a pixel-based and an object-based semi-automated supervised classification using monthly composites of Sentinel-2 imagery to achieve this detection. The pixel-based approach resource to the Maximum Entropy classifier while the object-based to an Artificial Neural Network. The overall accuracies range from 96.5% to 97.5%, which are promising results. Both methods can be combined for optimal detection over the whole territory.

Original languageEnglish
Title of host publicationProceedings - 2020 International Young Engineers Forum, YEF-ECE 2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781728156781
Publication statusPublished - Jul 2020
Event2020 International Young Engineers Forum, YEF-ECE 2020 - Online, Caparica, Portugal
Duration: 3 Jul 20203 Jul 2020


Conference2020 International Young Engineers Forum, YEF-ECE 2020


  • Artificial Neural Network
  • Change detection
  • Fuel Breaks
  • Machine Learning
  • Maximum Entropy
  • Object-based supervised classification
  • Pixel-based supervised classification


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