%0 Conference Proceedings
%T Fuel Break Monitoring with Sentinel-2 Imagery and GEDI Validation
%+ Centre of Technology and Systems (CTS)
%+ Forest Research Centre [Lisbon]
%A Pereira-Pires, João, E.
%A Aubard, Valentine
%A Baldassarre, G.
%A Fonseca, José, M.
%A Silva, João
%A Mora, André
%Z Part 3: Cyber-Physical IoT Systems in Wildfire Context
%< avec comité de lecture
%@ 978-3-030-96465-8
%( IFIP Advances in Information and Communication Technology
%B 4th IFIP International Internet of Things Conference (IFIPIoT)
%C Virtual, Netherlands
%Y Luis M. Camarinha-Matos
%Y Geert Heijenk
%Y Srinivas Katkoori
%Y Leon Strous
%I Springer International Publishing
%3 Internet of Things. Technology and Applications
%V AICT-641
%P 67-85
%8 2021-11-04
%D 2021
%R 10.1007/978-3-030-96466-5_5
%K Sentinel-2
%K Change detection
%K Fuel breaks
%K Remote sensing
%K Wildfires
%K Vegetation recovery
%K Fuel treatments
%K GEDI
%K Artificial neural network
%K Spectral indices
%Z Computer Science [cs]Conference papers
%X Mediterranean Europe is strongly affected by wildfires. In Portugal, the Portuguese Institute for Nature Conservation and Forests (ICNF) implemented the national fuel break (FB) network responsible for fire control and suppression. FBs are regions where vegetation is reduced to break up the fuel continuity and create pathways for the firefighting vehicles. The efficiency of this strategy relies on the correct implementation of FBs and on periodic fuel treatments. Multispectral imagery from Sentinel-2 (with high temporal and spatial resolution) facilitates the monitoring of FBs and the implementation of methodologies for their management. In this paper a two stages methodology is proposed for monitoring FBs. The first stage consists in detecting fuel treatments in FBs, to understand if those were correctly executed. This is done through a change detection methodology with resource to an Artificial Neural Network. The second stage monitors the vegetation recovery after a fuel treatment, to aid the scheduling of new treatments, ensuring the efficiency of FBs during the fire season. Both methodologies resort to reflectance bands and spectral indices from Sentinel-2; and timeseries and objects, exploiting the temporal and spatial information. The two stages were tested in different regions across the Portuguese territory, demonstrating their usability for all the national fuel break network. The detection of treatments achieved a relative error lower than 4%, and the vegetation recovery cycle estimated by the second stage match the expectations from ICNF.
%G English
%2 https://inria.hal.science/hal-04471535/document
%2 https://inria.hal.science/hal-04471535/file/512272_1_En_5_Chapter.pdf
%L hal-04471535
%U https://inria.hal.science/hal-04471535
%~ IFIP
%~ IFIP-AICT
%~ IFIP-IOT
%~ IFIP-AICT-641