An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires

Valero, Miguel Ángel; Rios, Oriol; Mata, Christian; Pastor, Elsa; Planas, Eulàlia

doi:10.1016/j.firesaf.2017.03.085

Cited by 29 publications

(15 citation statements)

References 41 publications

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“…. Springer 2018 [146] An integrated approach for tactical monitoring and data-driven spread Microwave radiometry imaging for forest fire detection: A simulation study Bonafoni S., Alimenti F., Angelucci G., Tasselli G. MDPI 2011 15 [120] Modelling the probability of lightning-induced forest fire occurrence in the province of León (NW Spain) [Modelización de la probabilidad de ocurrencia de incendios forestales por rayo en la provincia de León (NO de España)]…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Decision Support System Development of Wildland Fire: A Systematic Mapping

Vásquez

Cravero

Castro

et al. 2021

Forests

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Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last decade, different technologies have been applied to fight them. Selecting a specific technology and Decision Support Systems (DSS) is fundamental, since the results and validity of this could drastically oscillate according to the different environmental and geographic factors of the terrain to be studied. Given the above, a systematic mapping was realized, with the purpose of recognizing the most-used DSS and context where they have been applied. One hundred and eighty-three studies were found that used different types of DSS to solve problems of detection, prediction, prevention, monitoring, simulation, administration, and access to routes. The concepts key to the type of solution are related to the use or development of systems or Information and Communication Technologies (ICT) in the computer science area. Although the use of BA and Big Data has increased in recent years, there are still many challenges to face, such as staff training, the friendly environment of DSS, and real-time decision-making.

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Section: Conflicts Of Interestmentioning

confidence: 99%

Decision Support System Development of Wildland Fire: A Systematic Mapping

Vásquez

Cravero

Castro

et al. 2021

Forests

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“…automated wildfire monitoring. In [10], the proposed system is able to track wildfire perimeters from images acquired by a UAV and use this information to improve the parameters of a wildfire propagation simulator. Such wildfire prognosis capability could be integrated in an automated fire decision support tool, but means of this kind still have some way to go before their use in real operational situations.…”

Section: Wildfires and Uav Missionsmentioning

confidence: 99%

Wildfire remote sensing with UAVs: A review from the autonomy point of view

Bailon-Ruiz

Lacroix

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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This article analyses the state of the art on wildfire remote sensing using UAVs, an application context that has now gained significant interest. It reviews a selection of relevant publications, and proposes a classification scheme to synthesize them from an autonomy perspective. Three metrics are introduced: situation awareness, decisional ability, and collaboration ability. A discussion about the current state and the outlook of UAV systems for wildfire observation concludes the paper.

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“…However, there is limited research into optimal ways to apply performance metrics for model development. Quantitative evaluations of model performance have been used for calibrating models in real-time using data assimilation [53][54][55], however the focus of these has been to enhance predictions rather than permanently improve the model itself [20,[24][25][26]. To date, there has been limited development in how to apply these metrics for model development.…”

Section: Introductionmentioning

confidence: 99%

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

Duff

Cawson

Cirulis

et al. 2018

Forests

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Faster than real-time wildland fire simulators are being increasingly adopted by land managers to provide decision support for tactical wildfire management and assist with strategic risk planning. These simulators are typically based on simple forward rate-of-spread algorithms that were predominantly developed using observations of experimental fires. Given their operational use, it is important that fire simulators be assessed in terms of their performance for their intended use; predicting the spatial progression of wildfires. However, the conditions under which wildfires occur cannot be easily replicated experimentally. We describe and demonstrate a method for use in model development, whereby a dataset comprised of wildfire case-studies is used for evaluating the predictive performance of fire simulators. Two different versions of the model PHOENIX RapidFire were assessed, one incorporating a novel algorithm that accounts fine-scale spatial variation in landscape dryness. Evaluation was done by comparing simulator predictions against contemporaneous observations of 9 different wildfires that occurred in Australia. Performance was quantified using the sum of the Area Difference Indices-a measure of prediction overlap calculated for each prediction/observation pair. The two versions of the model performed similarly, with the newer version being marginally (but not statistically significantly) better when outcomes were summarised across all fires. Despite this, it did not perform better in all cases, with three of the 9 fires better predicted using the original model. Wildfire evaluation datasets were demonstrated to provide valuable feedback for model development, however the limited availability of data means power is lacking for detailed comparisons. With increasingly extreme weather conditions resulting from climate change, conditions under which wildfires occur are likely to continue to extend well beyond those under which fire models algorithms were developed. Consequently, the adoption of improved methods for collecting and utilising wildfire data is critical to ensure fire simulators retain relevance.

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An integrated approach for tactical monitoring and data-driven spread forecasting of wildfires

Cited by 29 publications

References 41 publications

Decision Support System Development of Wildland Fire: A Systematic Mapping

Decision Support System Development of Wildland Fire: A Systematic Mapping

Wildfire remote sensing with UAVs: A review from the autonomy point of view

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

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