Remote sensing monitoring of forest fire hazard based on random forest method
- Pages: 1-15(2024)
Published Online： 12 March 2024
DOI： 10.11834/jrs.20243323
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摘要
近年来森林火灾发生频繁，给人们正常工作生活以及自然生态系统带来了很大的影响。火险的评估对森林火灾的预防，消防资源的配置有着重要的意义。本文通过收集国内的历史森林火灾事件，综合不同地区的气象因子、植被指数、地形因子等，利用随机森林方法，建立了一个综合的火险评估模型。研究中，火灾事件结合FIRMS数据选取，火灾影响因素则由不同数据产品计算得到，气象因子、地形因子、植被指数分别利用ERA5-land数据、SRTM DEM产品和MODIS反射率产品MCD43A4计算得到。从测试案例结果来看，所建立的火险评估模型准确性比较高，ROC曲线下面积达到了0.84，在火险时序预测以及火险空间分布评估方面都取得较好的效果。虽然火灾发生的区域不同，影响火灾发生的因素也不同，但是火险值均在火灾发生前一周较高，其他时间火险值较低；火险空间分布比较合理，火灾发生区域的火险值从火灾发生前两个月至火灾发生当天逐渐升高。本文建立的火险评估模型涉及指标较全面，可以比较准确评估火险情况，同时可以应用在中国不同地区，部分解决地域限制性问题。
Abstract
In recent years
the frequent occurrence of forest fires has brought great impact to people's normal work and life as well as the natural ecosystem. The assessment of fire hazard is of great significance to the prevention of forest fire and the allocation of fire resources. This paper collects the historical forest fire events in China from 2002 to 2020
which are distributed in 5 climate regions in China: plateau mountain climate
temperate continental climate
temperate monsoon climate
subtropical monsoon climate
and tropical monsoon climate.
integrated meteorological factors
vegetation index
topographic factors in different regions
and used the random forest method to establish a comprehensive forest fire hazard assessment model. Fire influencing factors were calculated from different data products
fire events were selected using FIRMS images
meteorological factors were calculated using ERA5-land data
topographic factors were calculated using SRTM DEM products
and vegetation indices were calculated using MODIS reflectance product MCD43A4. The fire hazard assessment model can predict the time series of fire hazard and evaluate the spatial distribution of fire hazard. The fire occurrence location of the test data is different from that of the training data. From the test case results
the accuracy of the established fire hazard assessment model is relatively high
and the area under the ROC curve reaches 0.84
which achieves good results in the time series prediction of forest fire hazard and the spatial distribution assessment of forest fire hazard. Also
the predicted fire hazard value is close to the pre-calibrated fire hazard value. The results of time series prediction of fire hazard and evaluation of fire hazard spatial distribution are good
which are close to the actual situation. At the same time
the model established in this paper ranked the importance of the factors affecting the occurrence of fires. The most important factor was the annual diurnal sequence factor
reflecting the seasonal factor
followed by the moisture factor and the growth of vegetation
and the importance of topographic factors was lower. Importance ranking can help to understand the driving effect of different factors on the occurrence of fires
and understand which factors have a greater impact on the occurrence of forest fires. Although the area of forest fire occurrence is different and the factors affecting the fire occurrence are different
the change rule of fire hazard value was similar
the fire hazard value is higher in the week before the fire
and the fire hazard value is lower in other times. The spatial distribution of fire hazard is reasonable
and the fire hazard value in the fire area gradually increases from two months before the fire to the day of the fire. Also
the fire hazard in the same area 1 year before was significantly lower than the fire hazard value on the day of the fire
which can accurately assess the fire hazard situation. The forest fire hazard assessment model established in this paper involves comprehensive indicators
which can accurately assess the fire hazard situation. At the same time
it can be applied to different regions in China to partially solve the problem of regional restrictions.
关键词
森林火灾火险遥感随机森林危险监测
Keywords
Forest firefire hazardRemote sensingrandom foresthazard monitoring
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