通过卫星镜头观察一个长期高大桉树通量点的丛林火灾恢复情况:结合多尺度数据深入了解结构功能

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
William Woodgate , Stuart Phinn , Timothy Devereux , Raja Ram Aryal
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引用次数: 0

摘要

卫星对地观测(EO)数据在量化跨时空尺度的植被结构和功能指标方面发挥着至关重要的作用。然而,卫星光谱信号与光合作用速率之间的耦合程度(根据初级生产力总值(GPP)估算)在丛林火灾前后仍未得到充分研究,而这些都是全球碳循环的重要组成部分。这项研究评估了被动光学和主动激光雷达卫星数据的组合,以量化大火事件后光合作用的干扰和恢复情况。这项工作是在 2019 年 12 月一场灾难性丛林大火之后的 Tumbarumba 长期高大桉树通量站点完成的。对 TROPOMI 太阳诱导荧光(SIF)和哨兵 2 得出的绿度和烧伤严重程度指标(NDVI、EVI、NIRv 和 NBR)进行了研究,在此称为 "光谱指标"。我们发现,在火灾后一年和两年,植被光谱指标的恢复速度大大超过了植被总生产力的恢复速度。具体来说,与火灾前的水平相比,SIF 恢复了 80-90%,而 GPP 仅恢复了 45-50%。这表明,需要对火灾前和火灾后的数据分别使用 SIF:GPP 函数,以解释冠层结构和物种组成变化所导致的不同恢复轨迹。与传统的基于绿度的指数相比,使用 TROPOMI SIF 监测季节(月)尺度的冠层生产力更具优势,因为 SIF 可以跟踪火灾前后的 GPP 季节性。空载 GEDI 激光雷达数据有效捕捉了火灾后森林结构的变化,尽管采样间隔的时空范围较小,但揭示了林上植被密度的显著降低和林下植被密度的同时增加。与火灾前相比,由于林下物种的光利用效率较低,这导致了碳吸收量的减少,这一点通过现场气体交换测量得到了验证。总之,这项研究强调了考虑干扰历史以及林上植被和林下植被的相对丰度对于通过卫星平台跟踪 GPP 的重要性。我们的研究结果还强调了纵向野外数据在校准和验证 EO 数据方面的关键作用,并最终加深了我们对森林恢复过程的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bushfire recovery at a long-term tall eucalypt flux site through the lens of a satellite: Combining multi-scale data for structural-functional insight
Satellite earth observation (EO) data plays a vital role quantifying vegetation structural and functional metrics across spatio-temporal scales. However, the degree of coupling between satellite derived spectral signals and the rate of photosynthesis, as estimated by Gross Primary Productivity (GPP), both before and after bushfire remain understudied, yet these are a critical part of the global carbon cycle. This study evaluated a combination of passive optical and active LiDAR satellite data to quantify the disturbance and recovery of photosynthesis from a major fire event. The work was completed at the Tumbarumba long-term tall eucalypt flux site following a catastrophic bushfire in December 2019. TROPOMI solar-induced fluorescence (SIF) and Sentinel 2 derived greenness and burn severity metrics (NDVI, EVI, NIRv, and NBR) were investigated, termed ‘spectral metrics’ herewith. Detailed in-situ observations from leaf-to-canopy scales were utilised to examine variations in vegetation structural-functional parameters.
We found the rate of vegetation spectral metrics recovery largely outpaced GPP recovery at the one- and two-year post-fire mark. Specifically, SIF recovered to 80–90 % compared to pre-fire levels, whereas GPP recovered only 45–50 %. This indicated that separate SIF:GPP functions were required for pre- and post-fire data to account for different recovery trajectories due to changes in canopy structure and species composition. The use of TROPOMI SIF for monitoring canopy productivity at seasonal (monthly) time-scales was advantageous over traditional greenness-based indices, as SIF tracked GPP seasonality both pre- and post-fire. Spaceborne GEDI LiDAR data effectively captured post-fire changes in forest structure, albeit at sparse spatio-temporal sampling intervals, revealing a significant reduction in overstorey vegetation density and a concurrent increase in understorey vegetation density. This contributed to reduced carbon uptake, compared to pre-fire, due to the lower light use efficiency of understorey species, which was verified with in-situ gas exchange measurements. Overall, this study highlights the importance of accounting for disturbance history and the relative abundance of overstorey and understorey vegetation for tracking GPP from satellite platforms. Our results also highlight the crucial role of longitudinal field-based data for calibration and validation of EO data, ultimately enhancing our understanding of forest recovery processes.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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