A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency

• Published in: GIScience and remote sensing Vol. 59; no. 1; pp. 2019 - 2035
• Main Authors: Kang, Yoojin, Jang, Eunna, Im, Jungho, Kwon, Chungeun
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 31.12.2022; Taylor & Francis Group
• Subjects: algorithms; convolutional neural network; fire detection; Forest fire; forest fires; forests; Himawari-8 AHI; machine learning; neural networks; random forest; remote sensing; satellites; spatial data; statistical analysis
• DOI: 10.1080/15481603.2022.2143872
• ISSN: 1548-1603; 1943-7226

Although remote sensing of active fires is well-researched, their early detection has received less attention. Additionally, simple threshold approaches based on contextual statistical analysis suffer from generalization problems. Therefore, this study proposes a deep learning-based forest fire detection algorithm, with a focus on reducing detection latency, utilizing 10-min interval high temporal resolution Himawari-8 Advanced Himawari Imager. Random forest (RF) and convolutional neural network (CNN) were utilized for model development. The CNN model accurately reflected the contextual approach adopted in previous studies by learning information between adjacent matrices from an image. This study also investigates the contribution of temporal and spatial information to the two machine learning techniques by combining input features. Temporal and spatial factors contributed to the reduction in detection latency and false alarms, respectively, and forest fires could be most effectively detected using both types of information. The overall accuracy, precision, recall, and F1-score were 0.97, 0.89, 0.41, and 0.54, respectively, in the best scheme among the RF-based schemes and 0.98, 0.91, 0.63, and 0.74, respectively, in that among the CNN-based schemes. This indicated better performance of the CNN model for forest fire detection that is attributed to its spatial pattern training and data augmentation. The CNN model detected all test forest fires within an average of 12 min, and one case was detected 9 min earlier than the recording time. Moreover, the proposed model outperformed the recent operational satellite-based active fire detection algorithms. Further spatial generality test results showed that the CNN model had reliable generality and was robust under varied environmental conditions. Overall, our results demonstrated the benefits of geostationary satellite-based remote sensing for forest fire monitoring.