利用 NASA GEDI 数据了解火灾严重程度对森林结构和生物质储量的影响。西班牙和葡萄牙 2020 和 2021 年野火季节的启示

IF 5.7 Q1 ENVIRONMENTAL SCIENCES
Juan Guerra-Hernández , José M.C. Pereira , Atticus Stovall , Adrian Pascual
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引用次数: 0

摘要

野火逐渐削弱了森林的碳汇能力,加速了气候变化对森林生物多样性的影响,尤其是在地中海等大火经常发生且频率增加的地区。全球生态系统动态调查(GEDI)任务提供的数据可以为森林结构的变化提供信息,从而为火灾对植被的影响提供信息。在这项研究中,我们利用西班牙和葡萄牙 2020/21 年夏季火灾季节的数据,评估了 GEDI 在测量野火造成的生物量和结构变化方面的性能。GEDI 混合推断法用于计算火灾前和火灾后阶段的平均生物量和总生物量,而 GEDI 的足迹数据则进一步用于解释从光学数据中得出的火灾严重程度等级。我们的研究结果表明,随着火灾严重程度的增加,野火对生物量存量和 GEDI 生态指标的影响也越来越大。比起生物量存量,严重的火灾还极大地改变了植物面积体积密度等结构指标的变化趋势。通过整合 GEDI 指标来解释火灾严重程度,考虑到五个严重程度等级,准确率为 52%,考虑到三个主要等级(未燃烧、中度和高度),准确率为 69%。GEDI 的结构度量可用于改进全球使用的基于光学的火灾严重程度估算,并根据研究中展示的 GEDI 轨迹时间序列评估潜在的火灾影响,还可用于测量火灾季节之间的森林恢复情况。GEDI 的扩展是对野火绘图工作、捕捉火灾对森林生物多样性日益增加的影响的综合方法以及碳储量变化监测的重要支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of fire severity on forest structure and biomass stocks using NASA GEDI data. Insights from the 2020 and 2021 wildfire season in Spain and Portugal

Wildfires have been progressively shrinking the C sink capacity of forest accelerating climate change effects on forest biodiversity, especially where megafires are recurrent and of increased frequency such as in the Mediterranean. Data from The Global Ecosystem Dynamics Investigation (GEDI) mission can inform on changes on forest structure to inform on fire impacts on vegetation. In this study, we assessed the performance of GEDI at measuring biomass and structural change from wildfires using the 2020/21 summer fire seasons in Spain and Portugal. The GEDI hybrid-inference method was used to calculate mean and total biomass in pre- and post-fire stages, while GEDI footprint data was further used to explain the fire severity classes derived from optical data. Our results showed the increasing impact of wildfires on biomass stocks and GEDI ecological metrics by increasing fire severity. More than over biomass stocks, severe fires substantially altered trends in structural metrics such as plant area volume density. The integration of GEDI metrics to explain fire severity had an accuracy of 52% considering five severity classes and an accuracy of 69% when considering the three main classes: unburned, moderate and high. Structural metrics from GEDI can be used to improve optical-based fire severity estimates used globally and to evaluate potential fire impacts based on time-series of GEDI tracks as showcased in the study, but also to measure forest recovery between fire seasons. The extension of GEDI is a major support for wildfire mapping efforts, integrated approaches to capture the increasing impact of fire on forest biodiversity and the monitoring of changes in carbon stocks.

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