Next Article in Journal
Oak Decline Syndrome in Korean Forests: History, Biology, and Prospects for Korean Oak Wilt
Previous Article in Journal
Chemical Compositions of Walnut (Juglans Spp.) Oil: Combined Effects of Genetic and Climatic Factors
Previous Article in Special Issue
Estimation of Postfire Reforestation with SAR Polarimetry and NDVI Time Series
 
 
Article

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

by 1,2,3,*, 1,3, 1,3, 1,3, 1,3 and 1,3
1
School of Computer and Information, Hefei University of Technology, Hefei 230601, China
2
Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei 230009, China
3
Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Academic Editors: Olga Viedma and Chunying Ren
Forests 2022, 13(6), 963; https://doi.org/10.3390/f13060963
Received: 30 March 2022 / Revised: 17 June 2022 / Accepted: 17 June 2022 / Published: 20 June 2022
As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm. View Full-Text
Keywords: fire detection; genetic algorithm; change detection; FY-3D fire detection; genetic algorithm; change detection; FY-3D
Show Figures

Figure 1

MDPI and ACS Style

Dong, Z.; Yu, J.; An, S.; Zhang, J.; Li, J.; Xu, D. Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change. Forests 2022, 13, 963. https://doi.org/10.3390/f13060963

AMA Style

Dong Z, Yu J, An S, Zhang J, Li J, Xu D. Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change. Forests. 2022; 13(6):963. https://doi.org/10.3390/f13060963

Chicago/Turabian Style

Dong, Zhangyu, Jinqiu Yu, Sen An, Jin Zhang, Jinhui Li, and Daoli Xu. 2022. "Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change" Forests 13, no. 6: 963. https://doi.org/10.3390/f13060963

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop