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

Li, Xingdong; Gao, Hewei; Zhang, Mingxian; Zhang, Shiyu; Zhi-ming, Gao; Liu, Jiuqing; Sun, Shuliang; Hu, Tongxin; Sun, Lei

doi:10.3390/rs13214325

Cited by 16 publications

(8 citation statements)

References 46 publications

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“…Li et al [100] proposed a method for predicting the spread process of forest fires using a long-short-term memory (LSTM) [101] neural network model called FNU-LSTM. The authors collected video data of the forest fire spread process using an infrared camera mounted on a UAV and trained the LSTM network model to predict the forest fire spread rate.…”

Section: Monitoring Of Forest Firesmentioning

confidence: 99%

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

Zhang

Zhu

2023

Drones

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In recent years, UAV remote sensing has gradually attracted the attention of scientific researchers and industry, due to its broad application prospects. It has been widely used in agriculture, forestry, mining, and other industries. UAVs can be flexibly equipped with various sensors, such as optical, infrared, and LIDAR, and become an essential remote sensing observation platform. Based on UAV remote sensing, researchers can obtain many high-resolution images, with each pixel being a centimeter or millimeter. The purpose of this paper is to investigate the current applications of UAV remote sensing, as well as the aircraft platforms, data types, and elements used in each application category; the data processing methods, etc.; and to study the advantages of the current application of UAV remote sensing technology, the limitations, and promising directions that still lack applications. By reviewing the papers published in this field in recent years, we found that the current application research of UAV remote sensing research can be classified into four categories according to the application field: (1) Precision agriculture, including crop disease observation, crop yield estimation, and crop environmental observation; (2) Forestry remote sensing, including forest disease identification, forest disaster observation, etc.; (3) Remote sensing of power systems; (4) Artificial facilities and the natural environment. We found that in the papers published in recent years, image data (RGB, multi-spectral, hyper-spectral) processing mainly used neural network methods; in crop disease monitoring, multi-spectral data are the most studied type of data; for LIDAR data, current applications still lack an end-to-end neural network processing method; this review examines UAV platforms, sensors, and data processing methods, and according to the development process of certain application fields and current implementation limitations, some predictions are made about possible future development directions.

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Section: Monitoring Of Forest Firesmentioning

confidence: 99%

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

Zhang

Zhu

2023

Drones

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“…In order to ensure the applicability of the proposed model in different scenarios, we collected a variety of combustibles representative of Northeast China, such as conifer, camphor pine, and poplar leaves [44], for experiments. Moreover, we arranged different laying factors, such as the combustible load and bed depth, to simulate diverse environmental variables present during the actual forest fire spread [45]. Each experimental fire continued until all combustible materials had burned out, which typically lasted around 8-10 min depending on the laid combustible area as well as the wind speed and direction at the scene.…”

Section: Small-scale Ground Fire Experimentsmentioning

confidence: 99%

Predicting the Continuous Spatiotemporal State of Ground Fire Based on the Expended LSTM Model with Self-Attention Mechanisms

Wang

Zhang

et al. 2023

Fire

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Fire spread prediction is a crucial technology for fighting forest fires. Most existing fire spread models focus on making predictions after a specific time, and their predicted performance decreases rapidly in continuous prediction due to error accumulation when using the recursive method. Given that fire spread is a dynamic spatiotemporal process, this study proposes an expanded neural network of long short-term memory based on self-attention (SA-EX-LSTM) to address this issue. The proposed model predicted the combustion image sequence based on wind characteristics. It had two detailed feature transfer paths, temporal memory flow and spatiotemporal memory flow, which assisted the model in learning complete historical fire features as well as possible. Furthermore, self-attention mechanisms were integrated into the model’s forgetting gates, enabling the model to select the important features associated with the increase in fire spread from massive historical fire features. Datasets for model training and testing were derived from nine experimental ground fires. Compared with the state-of-the-art spatiotemporal prediction models, SA-EX-LSTM consistently exhibited the highest predicted performance and stability throughout the continuous prediction process. The experimental results in this paper have the potential to positively impact the application of spatiotemporal prediction models and UAV-based methods in the field of fire spread prediction.

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“…The method to analyze and identify the spread of the haze in the air due to wildfire has been elaborated and discussed by [13][14][15] to determine how much the area is being polluted by poor air quality. A deep learning algorithm called LSTM implements modeling to plot the pattern of the fire hotspot data, but the forecasting in this work only covers a small area or designated specific zone.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Hotspots Forecasting and Mapping for Environmental Monitoring Based on the Long Short-Term Memory Networks Deep Learning Algorithm

et al. 2023

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Global warming is raising the earth’s temperature, and resulting in increased forest fire events, especially in tropical regions with locations that are at high risk of wild and forest fires. Indonesia is a country in Southeast Asia that has experienced a severe number of wildfires, which have dangerous impacts on neighboring countries due to the emission of carbon and haze to the free air. The objective of this research is to map and plot the locations that consist of a significant number of fire hotspots and forecast the possible forest fire disasters in Indonesia based on the collected data of forest fires. The results of forecasting data are beneficial for the government and its policymakers to take preventive action and countermeasures regarding this wildfire issue. The Long Short-Term Memory (LSTM) algorithm, a deep learning method, was applied to analyze and then forecast the number of wildfire hotspots. The wildfire hotspot dataset from the year 2010 to 2022 is derived from the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS). The total number of collected observations is more than 700,000 wildfire data in Indonesia. The distribution of wildfire hotspots as shown in the results is concentrated mainly on two big islands, Kalimantan and Sumatra, Indonesia. The main issue is the peat type of land that is prone to spreading fire. Forecasting the number of hotspots for 2023 has achieved good results with an average error of 7%. Additionally, to prove that the proposed algorithm is working well, a simulation has been conducted using training data from 2018 to 2022 and testing data from 2021 to 2022. The forecasting result achieved a similar pattern of the number of fire hotspots compared to the available data in 2021 and 2022.

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

Cited by 16 publications

References 46 publications

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications

Predicting the Continuous Spatiotemporal State of Ground Fire Based on the Expended LSTM Model with Self-Attention Mechanisms

Wildfire Hotspots Forecasting and Mapping for Environmental Monitoring Based on the Long Short-Term Memory Networks Deep Learning Algorithm

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