Spatio-temporal analysis of fire occurrence in Australia

Valente, Fernanda; Laurini, Márcio Poletti

doi:10.1007/s00477-021-02043-8

Cited by 18 publications

(20 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the value is 1, vegetation factors have no effect on the ROS. When the value is in the range of (1,2], vegetation factors promote the ROS.…”

Section: Correction Rules Of Vegetationmentioning

confidence: 99%

“…Forest fire is a global natural disaster [1]. In recent years, the frequent occurrence of forest fires has been caused by global warming, the annual increase in the amount of combustible materials, and the difficulty controlling fire source [2,3]. There are many complex factors influencing the spread of forest fires [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…. The subfigures are related to the data slots of No (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). in Table2, respectively.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

View full text Add to dashboard Cite

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

show abstract

“…When the value is 1, vegetation factors have no effect on the ROS. When the value is in the range of (1,2], vegetation factors promote the ROS.…”

Section: Correction Rules Of Vegetationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

View full text Add to dashboard Cite

show abstract

“…As an initial illustration of the method's practical relevance, we here present an incomplete list of recent applications. In the time-period of May-Sep 2021, we find applications of the SPDE-approach to Gaussian fields in astronomy (Levis et al, 2021), health (Mannseth et al, 2021;Scott, 2021;Moses et al, 2021;Bertozzi-Villa et al, 2021;Moraga et al, 2021;Asri and Benamirouche, 2021), engineering (Zhang et al, 2021), theory (Ghattas and Willcox, 2021;Sanz-Alonso and Yang, 2021a;Lang and Pereira, 2021;Bolin and Wallin, 2021), environmetrics Beloconi et al, 2021;Vandeskog et al, 2021a;Wang and Zuo, 2021;Wright et al, 2021;Gómez-Catasús et al, 2021;Valente and Laurini, 2021b;Bleuel et al, 2021;Florêncio et al, 2021;Valente and Laurini, 2021a;Hough et al, 2021), econometrics (Morales and Laurini, 2021;Maynou et al, 2021), agronomy (Borges da Silva et al, 2021), ecology (Martino et al, 2021;Sicacha-Parada et al, 2021;Williamson et al;Bell et al, 2021;Humphreys et al;Xi et al, 2021;Fecchio et al), urban planning (Li, 2021), imaging (Aquino et al, 2021), modelling of forest fires (Taylor et al; Lindenmayer et al), fisheries (Babyn et al, 2021;van Woesik and Cacciapaglia, 2021;Jarvis et al, 2021;…”

Section: Some Recent Applicationsmentioning

confidence: 99%

The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running

Lindgren¹,

Bolin²,

Rue³

2021

Preprint

View full text Add to dashboard Cite

Gaussian processes and random fields have a long history, covering multiple approaches to representing spatial and spatio-temporal dependence structures, such as covariance functions, spectral representations, reproducing kernel Hilbert spaces, and graph based models. This article describes how the stochastic partial differential equation approach to generalising Matérn covariance models via Hilbert space projections connects with several of these approaches, with each connection being useful in different situations. In addition to an overview of the main ideas, some important extensions, theory, applications, and other recent developments are discussed. The methods include both Markovian and non-Markovian models, non-Gaussian random fields, non-stationary fields and space-time fields on arbitrary manifolds, and practical computational considerations.

show abstract

“…Identification and mapping of burned areas are essential tasks for monitoring the Earth surface, where satellite images are crucial sources of information [1,2]. More specifically, the generation of global burned area (BA) products has been an important issue since the late 1990s, but only since the 2000s we can find periodical and global BA products routinely derived with different degrees of accuracy, yet not for all over the Earth.…”

mentioning

confidence: 99%

Detection of burned areas with extreme gradient boosting algorithm in a multi-temporal setting (MBAGB)

Militino

Goyena

Pérez-Goya

et al. 2022

Preprint

View full text Add to dashboard Cite

Wildland fires are one of the most important disturbances on Earth ecosystems, where the combination of remote sensing data and modern machine learning techniques offer a great potential for detection and monitoring of burned areas. Many algorithms have been developed in parallel to locate burned areas at local and global scales, but more accurate methodologies are still needed to provide precise and valuable information to climate users, environmentalists and public administrations. Gradient boosting-based methodologies have shown a good performance when dealing with segmentation of burned areas. In this paper, we propose a new method, called MBAGB, based on these methodologies for detecting and mapping burned areas in a multi-temporal setting of satellite imagery. We illustrate the procedure with the fires occurred in October 2017 in a region covering the North-Central Portugal and the North-West of Spain. Daily satellite images used for the definition of spectral indexes are taken from MODIS satellite products, between September and November 2017. The MBAGB accuracy metrics show the overall goodness of this method, and in particular, a very small number of false negatives in the identification of burned areas.

show abstract

Spatio-temporal analysis of fire occurrence in Australia

Cited by 18 publications

References 42 publications

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running

Detection of burned areas with extreme gradient boosting algorithm in a multi-temporal setting (MBAGB)

Contact Info

Product

Resources

About