Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditions
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- Toward an adaptable deep-learning model for satellite-based wildfire monitoring with consideration of environmental conditions
- Kang, Yoojin; Sung, Taejun; Im, Jungho
- Issue Date
- ELSEVIER SCIENCE INC
- REMOTE SENSING OF ENVIRONMENT, v.298, pp.113814
- As the majority of active fire detection algorithms have been developed for worldwide applications using only satellite data without considering observing conditions and environmental factors, their performance varies regionally. This study investigates the viability of an adaptable active fire detection model that is applicable to diverse environmental and observing conditions by fusing numerical model data and satellite images. The model was developed for various land cover and climate types using commonly utilized brightness temperature-related variables (key variables) and supporting variables (sub-variables), including solar zenith angle, satellite zenith angle (SAZ), relative humidity (RH), and skin temperature. A dual-module (DM) convolutional neural network (CNN) structure was adopted to consider the different properties of key variables and sub-variables, and a control without sub-variables was used to assess the impact of observing and environmental variables. The proposed model was further evaluated using existing polar-orbiting and geostationary satellite-based active fire products. The recall and precision of the control model were 0.80 and 0.98, respectively, and the standard deviation of recall for the five focus sites was 0.140. However, the DM CNN model was notable for its higher recall and robustness compared to the control model (recall of 0.84, precision of 0.97, and standard deviation of recall of 0.126). High RH and SAZ, and the day-night transition period contributed to the poor performance of the control model which was mitigated by the DM CNN model. In particular, the use of RH improved the recall of the model, and SAZ contributed to the reduction of performance variation. Our model also outperformed the two geostationary satellite-based active fire products in terms of detection capacity, resulting in a spatial distribution of active fires similar to that of polar-orbiting satellite-based active fire products.
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