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Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2018-06-01 , DOI: 10.1016/j.rse.2018.03.019
Ran Meng , Jin Wu , Feng Zhao , Bruce D. Cook , Ryan P. Hanavan , Shawn P. Serbin

Abstract Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal scales. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management and constraining/benchmarking fire effect schemes in ecological process models.

中文翻译:

使用多传感器遥感技术测量混合松橡树林中燃烧严重程度梯度的短期火灾后森林恢复

摘要 了解火灾后森林恢复对于研究森林动态和全球碳循环至关重要。实地研究表明,森林恢复率对单个树木水平的燃烧严重程度呈凸反应,与火灾引起的树木死亡有关;然而,这些发现在空间/时间范围内受到限制,而传统光学遥感研究无法检测到,这主要归因于林下恢复的污染影响。在这里,我们检查了联合使用多传感器遥感技术(即 1 m 同时机载成像光谱和 LiDAR 以及 2 m 卫星多光谱图像)将冠层恢复与林下恢复分开是否能够量化火灾后森林恢复率跨越大范围烧伤严重程度的大梯度。我们的研究是在纽约长岛的一个松橡混交林中进行的,那是一场致命的火灾三年后。我们的研究远程检测到森林恢复率在整个燃烧区域的燃烧严重程度的初始增加然后下降,在中等森林燃烧严重程度等级下,基于冠层面积的最大恢复率为每年 10%。更有趣的是,这种远程检测到的凸性关系也存在于物种层面,松树对高烧伤的恢复能力更强,最大恢复率(每年 12%)比橡树(每年 4%)更高。这些结果是显示火灾适应策略对火灾后森林恢复影响的首批定量证据之一,源自相对较大的时空尺度。
更新日期:2018-06-01
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