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Study on Integration of Remote Sensing for Predicting Complicated Forest Fire Spread
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Version 1
: Received: 20 August 2024 / Approved: 20 August 2024 / Online: 20 August 2024 (14:24:22 CEST)
How to cite: Liu, P.; Zhang, G. Study on Integration of Remote Sensing for Predicting Complicated Forest Fire Spread. Preprints 2024, 2024081458. https://doi.org/10.20944/preprints202408.1458.v1 Liu, P.; Zhang, G. Study on Integration of Remote Sensing for Predicting Complicated Forest Fire Spread. Preprints 2024, 2024081458. https://doi.org/10.20944/preprints202408.1458.v1
Abstract
Forest fires can occur suddenly and have significant environmental, economic, and social consequences. Timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and scientifically extinguish fires. This paper provides a comprehensive analysis of advanced technologies for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model optimizes the method for acquiring parameters and enhances algorithm accuracy. By considering regional landforms (ridge lines, valley lines, slopes) and vegetation coverage, this paper establishes visual interpretation markers for identifying hotspots within the spread model. Utilizing statistical data from pixels within the fire line zone transforms the methodology for predicting forest fire spread speed, resulting in greater accuracy compared to conventional fixed-point predictions. Finally, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China and verified using high-time resolution mid-infrared image data and previous research findings. The results demonstrate that the predicted fire spread area from this RS-enabled model is consistent with both high-time resolution geostationary satellite data and previous research outcomes, indicating high reliability. This model can provide crucial fire spread information to relevant emergency departments, enabling effective preemptive measures and scientific firefighting strategies.
Keywords
forest fire spread; Wang Zhengfei model; remote sensing; prediction
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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