Visualization of 3D forest fire spread based on the coupling of multiple weather factors

Meng, Qingkuo; Huai, Yongjian; You, Jiawei; Nie, Xiaoying

doi:10.1016/j.cag.2022.12.002

Cited by 7 publications

(9 citation statements)

References 21 publications

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“…Zhang et al improved upon the Wang Zhengfei model of forest fire spread by incorporating the impact of fuel moisture on the speed of fire spread and refining the calculation of the initial fire spread rate [25]. Meng et al proposed a theoretical model of forest fire spread based on an improved principle of Huygens that also factors in weather elements such as wind speed, wind direction, and precipitation [26]. Zhang et al simplified the Rothermel forest fire spread rate formula and, combined with cellular automata, established a multi-dimensional cellular automata (MD-CA) model for forest fire spread containing different burning characteristics within each cell [27].…”

Section: Introductionmentioning

confidence: 99%

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

Chen,

Tian,

Zheng

et al. 2024

Forests

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Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of forest fire spread prediction are constrained by their ability to process multidimensional fire-related data, particularly in the integration of spatiotemporal information. To address these limitations and enhance the accuracy of forest fire spread prediction, we proposed the AutoST-Net model. This innovative encoder–decoder architecture combines a three-dimensional Convolutional Neural Network (3DCNN) with a transformer to effectively capture the dynamic local and global spatiotemporal features of forest fire spread. The model also features a specially designed attention mechanism that works to increase predictive precision. Additionally, to effectively guide the firefighting work in the southwestern forest regions of China, we constructed a forest fire spread dataset, including forest fire status, weather conditions, terrain features, and vegetation status based on Google Earth Engine (GEE) and Himawari-8 satellite. On this dataset, compared to the CNN-LSTM combined model, AutoST-Net exhibits performance improvements of 5.06% in MIou and 6.29% in F1-score. These results demonstrate the superior performance of AutoST-Net in the task of forest fire spread prediction from remote sensing images.

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Section: Introductionmentioning

confidence: 99%

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

Chen,

Tian,

Zheng

et al. 2024

Forests

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Section: Introductionmentioning

confidence: 99%

“…Based on [5], three main elements influencing forest fire behavior are combustibles, geography, and weather, and contemporary forest fire spread models must take weather aspects into full consideration. The study in [5] suggested a forest fire spread approach based on ambient meteorological elements, in order to give a graphical simulation of forest fire spread in the natural environment. The study also offered a forest fire spread technique based on environmental weather elements, in order to enhance the connected senses' sense of immersion and to enable realistic scene wandering.…”

Section: Introductionmentioning

confidence: 99%

Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Suhardiman,

Hermanto

2023

Proceedings of International Symposium on Grids &Amp; Clouds (ISGC) 2023 in Conjunction With HEPiX Spring 2023 Workshop — PoS(I

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Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of 'flame' status occurring. We conclude that the ANN method can give more accurate results than the existing classification.

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“…In recent years, forest fires have become prevalent natural disasters that countries, regions, and organizations around the world need to face [1]. Therefore, researchers have explored the principles of forest fire behavior to reduce the damage caused by forest fires to the national economy and the ecological environment, such as when Hädrich [2] fit the threedimensional geometric modeling of plants to capture the burning of individual trees and the propagation between trees; that of Pais [3] models units based on fuel, weather, humidity, and terrain attributes, further facilitating the prediction of the growth of individual fires.…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Spread Simulation and Fire Extinguishing Visualization Research

Meng

Huai

et al. 2023

Forests

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There are three main types of forest fires: surface fires, tree crown fires, and underground fires. The frequency of surface fires and tree crown fires accounts for more than 90% of the overall frequency of forest fires. In order to construct an immersive three-dimensional visualization simulation of forest fires, various forest fire ignition methods, forest fire spread, and fire extinguishing simulation exercises are studied. This paper proposes a lightweight forest fire spread method based on cellular automata applied to the virtual 3D world. By building a plant model library using cells to express different plants, and by building a 3D geometric model of plants to truly capture the combustion process of a single plant, we can further simulate forest-scale fire propagation and analyze the factors that affect forest fire spread. In addition, based on the constructed immersive forest scene, this study explored various forms of fire extinguishing methods in the virtual environment, mainly liquid flame retardants such as water guns, helicopter-dropped flame retardants, or simulated rainfall. Therefore, the forest fire occurrence, spread, and fire extinguishing process can be visualized after the interactive simulation is designed and implemented. Finally, this study greatly enhanced the immersion and realism of the 3D forest fire scene by simulating the changes in plant materials during the spread of a forest fire.

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Visualization of 3D forest fire spread based on the coupling of multiple weather factors

Cited by 7 publications

References 21 publications

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data

Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Forest Fire Spread Simulation and Fire Extinguishing Visualization Research

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