Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9
The rapid advancement of deep learning (DL) technology significantly enhances early forest fire detection methods. However, traditional approaches often rely on fixed thresholds and supervised learning techniques, which may fail to account for the complex spatiotemporal dynamics associated with fore...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10938890/ |
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| author | Zekun Xu Zhaoming Zhang Guojin He Shuaizhang Zhang Tengfei Long Guizhou Wang |
| author_facet | Zekun Xu Zhaoming Zhang Guojin He Shuaizhang Zhang Tengfei Long Guizhou Wang |
| author_sort | Zekun Xu |
| collection | DOAJ |
| description | The rapid advancement of deep learning (DL) technology significantly enhances early forest fire detection methods. However, traditional approaches often rely on fixed thresholds and supervised learning techniques, which may fail to account for the complex spatiotemporal dynamics associated with forest fire events. To overcome this limitation, an adaptive DL model is proposed and specifically designed for early forest fire monitoring. This model integrates Stacking ConvLSTM to forecast mid-infrared brightness temperatures and employs a nonparametric dynamic thresholding method based on Otsu's algorithm for spatiotemporal anomaly detection, facilitating the identification of potential hotspots. By effectively capturing complex dependencies within spatiotemporal dimensions, this method improves detection accuracy. Furthermore, high-confidence early fire points are determined through dual-band analysis and land cover detection. Comparative experiments utilizing Himawari-8/9 satellite data reveal that the proposed method outperforms traditional techniques as well as the latest temporal methods, achieving an accuracy of 0.995, precision of 0.985, recall of 0.946, and an F1 score of 0.965. In addition, our method demonstrates an average fire detection delay of just 7 min and an average runtime of 71 s, underscoring its effectiveness in early forest fire detection. This approach serves as a robust tool for enhancing forest fire monitoring systems, offering significant implications for reducing response times and mitigating fire-related damages. |
| format | Article |
| id | doaj-art-ca00b6a7240e4c0993566f3fce8299ad |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ca00b6a7240e4c0993566f3fce8299ad2025-08-20T02:26:23ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01189396940810.1109/JSTARS.2025.355489210938890Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9Zekun Xu0https://orcid.org/0009-0006-7297-0964Zhaoming Zhang1https://orcid.org/0000-0002-8779-2738Guojin He2Shuaizhang Zhang3Tengfei Long4Guizhou Wang5https://orcid.org/0000-0002-2347-8416Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, ChinaAerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, ChinaAerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, ChinaAerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, ChinaAerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, ChinaAerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, ChinaThe rapid advancement of deep learning (DL) technology significantly enhances early forest fire detection methods. However, traditional approaches often rely on fixed thresholds and supervised learning techniques, which may fail to account for the complex spatiotemporal dynamics associated with forest fire events. To overcome this limitation, an adaptive DL model is proposed and specifically designed for early forest fire monitoring. This model integrates Stacking ConvLSTM to forecast mid-infrared brightness temperatures and employs a nonparametric dynamic thresholding method based on Otsu's algorithm for spatiotemporal anomaly detection, facilitating the identification of potential hotspots. By effectively capturing complex dependencies within spatiotemporal dimensions, this method improves detection accuracy. Furthermore, high-confidence early fire points are determined through dual-band analysis and land cover detection. Comparative experiments utilizing Himawari-8/9 satellite data reveal that the proposed method outperforms traditional techniques as well as the latest temporal methods, achieving an accuracy of 0.995, precision of 0.985, recall of 0.946, and an F1 score of 0.965. In addition, our method demonstrates an average fire detection delay of just 7 min and an average runtime of 71 s, underscoring its effectiveness in early forest fire detection. This approach serves as a robust tool for enhancing forest fire monitoring systems, offering significant implications for reducing response times and mitigating fire-related damages.https://ieeexplore.ieee.org/document/10938890/ConvLSTMHimawair-8/9spatiotemporal anomaly detectionwildfire |
| spellingShingle | Zekun Xu Zhaoming Zhang Guojin He Shuaizhang Zhang Tengfei Long Guizhou Wang Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ConvLSTM Himawair-8/9 spatiotemporal anomaly detection wildfire |
| title | Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 |
| title_full | Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 |
| title_fullStr | Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 |
| title_full_unstemmed | Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 |
| title_short | Adaptive Early Wildfire Monitoring Based on Spatiotemporal Prediction and Himawari 8/9 |
| title_sort | adaptive early wildfire monitoring based on spatiotemporal prediction and himawari 8 x002f 9 |
| topic | ConvLSTM Himawair-8/9 spatiotemporal anomaly detection wildfire |
| url | https://ieeexplore.ieee.org/document/10938890/ |
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