LSTM neural network integrating remote sensing and meteorological data reveals forest fire risk trajectories in China over the past 100 years

IF 5.7 1区 农林科学 Q1 AGRONOMY
Xiong Yin , Mengqi Bai , Yuwen Peng , Bangqian Chen , Hongyan Lai , Weili Kou , Yue Chen , Jingyi Wang , Mingshi Li
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引用次数: 0

Abstract

Quantifying current forest fire risk is essential to ecological security and sustainable resource management, while reconstructing historical fire risk helps understand the impacts of climate change and human activities on forest fire regimes, enabling effective lessons from past forest management practices. However, the lack of long-term forest fire risk assessment studies (e.g., 1921 to 1960) is due to insufficient reliable spatio-temporally explicit forest fire records, stemming from the unavailability of spatial information technology for modeling fire occurrences. This study presented a retrospective fire risk modelling approach using deep learning, meteorological station observations, and spatial interpolation to overcome this limitation. By modifying forest fire danger index (MFFDI) and integrating it with a multi-step prediction Long Short-Term Memory model, historical station-wise MFFDI data for 1921–1960 was generated, leading to the creation of a forest fire risk distribution map via spatial interpolation that accounts for terrain and human influences. The results indicated that the model-predicted MFFDI for 1921 to 1960 demonstrated high accuracy, with a mean R2 of 0.77 and a mean MSE of 0.24 from an independent validation subset of the 778 meteorological stations across China. For the independent validation of the model-generated forest fire risk maps, 193 of the 265 historical fire events occurred in high or relatively high risk zones, and 72 in moderate risk zones. Spatial analysis revealed that over the past 100 years, high and relatively high risk areas were primarily located in southwest, northwest, and north China, comprising 19 % to 26 % of the total forest fire risk area. After 1980, high and relatively high risk zones gradually concentrated in the central and southwestern regions, while moderate risk zones shifted from the southwest to the southeast. The area of high risk zones was constantly stable, below 8 % over time. This study reconstructs historical fire risk maps, highlighting the century-long dynamics of forest fire risk in China, which is essential for formulating scientific fire control strategies.
结合遥感和气象数据的LSTM神经网络揭示了近百年来中国森林火灾风险的变化轨迹
量化当前的森林火灾风险对生态安全和可持续资源管理至关重要,而重建历史火灾风险有助于了解气候变化和人类活动对森林火灾制度的影响,从而从过去的森林管理实践中吸取有效经验。然而,缺乏长期森林火灾风险评估研究(例如,1921年至1960年)是由于缺乏可靠的时空明确的森林火灾记录,这是由于无法获得用于模拟火灾发生的空间信息技术。本研究提出了一种回顾性火灾风险建模方法,利用深度学习、气象站观测和空间插值来克服这一局限性。通过对森林火险指数(MFFDI)进行修正,并将其与多步预测长短期记忆模型相结合,生成了1921-1960年的历史站点MFFDI数据,并通过空间插值建立了考虑地形和人类影响的森林火险分布图。结果表明,在中国778个气象站的独立验证子集上,模型预测的1921 ~ 1960年MFFDI具有较高的精度,平均R2为0.77,平均MSE为0.24。为了对模型生成的森林火灾风险图进行独立验证,265个历史火灾事件中有193个发生在高风险或相对高风险地区,72个发生在中度风险地区。空间分析表明,近100 a来中国森林火险高发区和相对高发区主要分布在西南、西北和华北地区,占森林火险总面积的19% ~ 26%。1980年以后,高风险区和相对高风险区逐渐向中部和西南部集中,中度风险区由西南向东南转移。随着时间的推移,高风险区域的面积一直稳定在8%以下。本研究通过重建历史火险图,揭示了中国森林火险的百年动态,为制定科学的防火策略提供了重要依据。
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来源期刊
CiteScore
10.30
自引率
9.70%
发文量
415
审稿时长
69 days
期刊介绍: Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published. Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.
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