利用多时相Landsat和NEON激光雷达数据表征2016年Chimney Tops 2火灾的空间烧伤严重程度模式

T. Park, S. Sim
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摘要

2016年在大烟山国家公园(GSMNP)发生的“烟囱顶2号”野火(CT2)被记录为GSMNP历史上最大的火灾。了解燃烧严重程度的空间格局及其潜在的控制因素对于管理受影响的森林和减少未来的火灾风险至关重要;然而,这一点还没有得到很好的理解。在这里,我们提出了两个研究问题:1)表征CT2火灾中烧伤严重程度模式的最重要因素是什么?2)被动和主动光学遥感传感器的烧伤严重程度测量是否提供了一致的火灾损害视图?为了解决这些问题,我们使用了基于多时相Landsat和lidar的烧伤严重程度测量方法,即相对化差异归一化烧伤比(RdNBR)和相对化差异平均树高(RdMTH)。采用随机森林方法识别烧伤严重程度空间变异特征的关键驱动因素,并进一步评估每个解释变量的部分依赖性。研究发现,火前植被结构和地形在CT2火灾中异质性混合烧伤严重程度特征中都起着重要作用。平均树高、海拔和地形位置是解释烧伤严重程度变化的关键因素。我们从基于陆地卫星和激光雷达的烧伤严重程度测量中观察到大致一致的空间模式。然而,RdNBR和RdMTH之间的植被类型和结构依赖关系导致局部烧伤严重程度模式不一致,特别是在高RdNBR地区。我们的研究强调了火灾前植被结构和地形在理解烧伤严重程度模式中的重要作用,并敦促将光谱和结构变化结合起来,以全面绘制和了解火灾对森林生态系统的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data
The Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems.
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