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Article

Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind

1
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
2
Northern Forest Fire Management Key Laboratory of the State Forestry and Grassland Bureau, Northeast Forest University, Harbin 150040, China
3
College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China
4
College of Forestry, Northeast Forestry University, Harbin 150040, China
*
Author to whom correspondence should be addressed.
Academic Editor: Luis A. Ruiz
Remote Sens. 2021, 13(21), 4325; https://doi.org/10.3390/rs13214325
Received: 9 September 2021 / Revised: 17 October 2021 / Accepted: 24 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Remote Sensing and Smart Forestry)
Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires. View Full-Text
Keywords: UAV remote sensing; forest fire; fire spread modelling; LSTM; wind prediction UAV remote sensing; forest fire; fire spread modelling; LSTM; wind prediction
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MDPI and ACS Style

Li, X.; Gao, H.; Zhang, M.; Zhang, S.; Gao, Z.; Liu, J.; Sun, S.; Hu, T.; Sun, L. Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind. Remote Sens. 2021, 13, 4325. https://doi.org/10.3390/rs13214325

AMA Style

Li X, Gao H, Zhang M, Zhang S, Gao Z, Liu J, Sun S, Hu T, Sun L. Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind. Remote Sensing. 2021; 13(21):4325. https://doi.org/10.3390/rs13214325

Chicago/Turabian Style

Li, Xingdong, Hewei Gao, Mingxian Zhang, Shiyu Zhang, Zhiming Gao, Jiuqing Liu, Shufa Sun, Tongxin Hu, and Long Sun. 2021. "Prediction of Forest Fire Spread Rate Using UAV Images and an LSTM Model Considering the Interaction between Fire and Wind" Remote Sensing 13, no. 21: 4325. https://doi.org/10.3390/rs13214325

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