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Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Ding, Yunhong; Wang, Mingyang; Fu, Yujia; Wang, Qian

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds Thumbnail


Authors

Yunhong Ding

Mingyang Wang

Yujia Fu

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Qian Wang qian.wang@durham.ac.uk
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Abstract

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.

Journal Article Type Article
Acceptance Date May 7, 2024
Online Publication Date May 10, 2024
Publication Date May 10, 2024
Deposit Date Jun 12, 2024
Publicly Available Date Jun 12, 2024
Journal Forests
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 15
Issue 5
Article Number 839
DOI https://doi.org/10.3390/f15050839
Keywords forest fires, deep learning, smoke detection, machine learning, dynamic brightness temperature threshold
Public URL https://durham-repository.worktribe.com/output/2480535

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