A Deep Learning Framework: Predicting Fire Radiative Power From the Combination of Polar-Orbiting and Geostationary Satellite Data During Wildfire Spread

Fire radiative power (FRP) is a key indicator for evaluating the intensity of wildfires, unlike traditional real-time fire lines or combustion areas that only provide binary information, and its accurate prediction is more important for firefighting actions and environmental pollution assessment. To...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2024, Vol.17, p.10827-10841
Main Authors: Dong, Zixun, Zheng, Change, Zhao, Fengjun, Wang, Guangyu, Tian, Ye, Li, Hongchen
Format: Article
Language:English
Subjects:
Biological system modeling
Deep learning
Environmental assessment
Fire control
Fire fighting
fire radiative power (FRP)
Forestry
Performance indices
Performance prediction
Polar orbits
Population density
Predictions
Predictive models
Real time
Remote sensing
Satellites
spatiotemporal prediction
Synchronous satellites
Transfer learning
Vegetation index
Vegetation mapping
wildfire
Wildfires
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Summary:Fire radiative power (FRP) is a key indicator for evaluating the intensity of wildfires, unlike traditional real-time fire lines or combustion areas that only provide binary information, and its accurate prediction is more important for firefighting actions and environmental pollution assessment. To this end, we used a combination of data from geostationary satellites and polar orbit satellites to correct the FRP data. Incorporating various factors that affect wildfire spread, such as meteorological conditions, topography, vegetation indexes, and population density, we constructed a comprehensive California wildfire spread dataset, covering information since 2017. Then, we established a deep learning framework that integrates various modules to analyze multimodal data for the accurate prediction of FRP imagery. We investigated the impact of input sequence length and loss function design on model predictive performance, leading to subsequent model optimization. Furthermore, our model has demonstrated acceptable performance in transfer learning and multistep prediction, emphasizing its application value in wildfire prediction and management. It can provide more detailed information about wildfire spread, showcasing the powerful capability of deep learning to process multimodal data and its potential in the emerging field of real-time FRP prediction.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3403146