Integração de dados de diferentes satelites para mapear combustiveis florestais: o papel da detecção remota para uma efectiva gestão dos combustiveis florestais

Publications

A Near-Real-Time Operational Live Fuel Moisture Content (LFMC) Product to Support Decision-Making at the National Level

Publication . Benali, Akli; Baldassarre, Giuseppe; Loureiro, Carlos; Briquemont, Florian; Fernandes, Paulo M.; Rossa, Carlos; Figueira, Rui

Live fuel moisture content (LFMC) significantly influences fire activity and behavior over different spatial and temporal scales. The ability to estimate LFMC is important to improve our capability to predict when and where large wildfires may occur. Currently, there is a gap in providing reliable near-real-time LFMC estimates which can contribute to better operational decision-making. The objective of this work was to develop near-real-time LFMC estimates for operational purposes in Portugal. We modelled LFMC using Random Forests for Portugal using a large set of potential predictor variables. We validated the model and analyzed the relationships between estimated LFMC and both fire size and behavior. The model predicted LFMC with an R2 of 0.78 and an RMSE of 12.82%, and combined six variables: drought code, day-of-year and satellite vegetation indices. The model predicted well the temporal LFMC variability across most of the sampling sites. A clear relationship between LFMC and fire size was observed: 98% of the wildfires larger than 500 ha occurred with LFMC lower than 100%. Further analysis showed that 90% of these wildfires occurred for dead and live fuel moisture content lower than 10% and 100%, respectively. Fast-spreading wildfires were coincident with lower LFMC conditions: 92% of fires with rate of spread larger than 1000 m/h occurred with LFMC lower than 100%. The availability of spatial and temporal LFMC information for Portugal will be relevant for better fire management decision-making and will allow a better understanding of the drivers of large wildfires.

2025-04-30Journal article

Open access

Keywords

Wildfire,Remote Sensing,Fuel Models,ICESat-2, Agricultural sciences

Funding agency

Fundação para a Ciência e a Tecnologia, I.P.
Fundação para a Ciência e a Tecnologia, I.P.

Funding programme

3599-PPCDT
Concurso de Projetos de Investigação Científica e Desenvolvimento Tecnológico no Âmbito da Prevenção e Combate a Incêndios Florestais - 2019

Funding Award Number

PCIF/GRF/0116/2019