Mapping Forest Wildfire Risk of the World

Yu, Meng; Deng, Ying; Shi, Peijun

doi:10.1007/978-3-662-45430-5_14

Cited by 24 publications

(17 citation statements)

References 11 publications

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“…Wildland fires are complex phenomena with a large number of uncertain and highly unpredictable driving factors that still remain undiscovered [1,2]. In recent years, a surge in the loss of lives, property, and biodiversity caused by wildfires occurred worldwide [3,4]. Some estimations indicate that the future climate change and its effects on rainfall patterns and drought occurrences will further exacerbate wildfires in many parts of the world [5,6], which, in turn, places strong demands on the managers to adopt wildfire risk mitigation strategies in the face of future climate change.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

et al. 2019

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Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBFAUC = 0.701; LRAUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes.

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Section: Introductionmentioning

confidence: 99%

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

et al. 2019

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“…On the other hand, short-term risk is linked to changeable factors (vegetation stress or climate conditions) and is updated using a time scale that can eventually be on a daily or even hourly basis (real-time), with tactical and mainly operative usefulness [22]. From a geographic point of view, global wildfires studies are usually linked to long-term approaches and are mainly focused on world fire regimes identification or on the assessment of climate change influence over these regimes [23,24]. In the same way, regional risk mapping includes a wide range of spatial scenarios-several hundred thousand to millions of square kilometers [25]; they might include international [26,27], national [28] or subnational approaches [29], and they are suitable for national and regional orientation of environmental and forest polices and legislation.…”

Section: Introductionmentioning

confidence: 99%

Mapping Forest Fire Risk at a Local Scale—A Case Study in Andalusia (Spain)

2019

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Forest fires are a critical environmental problem facing current societies, with serious repercussions at ecological, economic and personal safety levels. Detailed maps enabling identification of areas liable to be affected is an indispensable first step allowing different prevention and protection measures vis-à-vis this kind of phenomenon. These maps could be especially valuable for use in land management and emergency planning at a municipality scale. A methodology is shown for producing local maps of mid- and short-term forest fire risk, integrating both natural and human factors. Among natural factors, variables normally used in hazard models are considered as fuel models, slopes or vegetation moisture stress. From the human perspective, more novel aspects have been evaluated, meant either to assess human-induced hazard (closeness to forestland of causative elements or the ability of people to penetrate the forest environment), or to assess vulnerability, considering the population’s location in urban centres and scattered settlements. The methodology is applied in a municipality of Andalusia (Spain) and obtained results were compared to burned areas maps.

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“…Converting RGB Images to YCbCr. Due to the fact that different kinds of moving objects can be included after applying background subtraction, such as trees, animals, birds, and people, therefore images from the background subtraction stage are converted to YCbCr [9] to select candidate fire regions using (3).…”

Section: Movement Containing Region Detection Based On Background Submentioning

confidence: 99%

“…Hundreds of millions of hectares are destroyed by wildfires each year [2] and over 200,000 forest fires happen every year in the world. Forest fires destroy a total area of 3.5 to 4.5 million km2 [3]. Increase in forest fires in forest areas around the world has resulted in an increased motivation for developing fire warning systems for the early detection of wildfires [1].…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

Mahmoud

Ren

2018

Mathematical Problems in Engineering

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Forest fires represent a real threat to human lives, ecological systems, and infrastructure. Many commercial fire detection sensor systems exist, but all of them are difficult to apply at large open spaces like forests because of their response delay, necessary maintenance needed, high cost, and other problems. In this paper a forest fire detection algorithm is proposed, and it consists of the following stages. Firstly, background subtraction is applied to movement containing region detection. Secondly, converting the segmented moving regions from RGB to YCbCr color space and applying five fire detection rules for separating candidate fire pixels were undertaken. Finally, temporal variation is then employed to differentiate between fire and fire-color objects. The proposed method is tested using data set consisting of 6 videos collected from Internet. The final results show that the proposed method achieves up to 96.63% of true detection rates. These results indicate that the proposed method is accurate and can be used in automatic forest fire-alarm systems.

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Mapping Forest Wildfire Risk of the World

Cited by 24 publications

References 11 publications

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Mapping Forest Fire Risk at a Local Scale—A Case Study in Andalusia (Spain)

Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

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