Western North America has seen a recent dramatic increase in large and often high‐severity wildfires. After forest fire, understanding patterns of structural recovery is important, as recovery patterns impact critical ecosystem services. Continuous forest monitoring provided by satellite observations is particularly beneficial to capture the pivotal post‐fire period when forest recovery begins. However, it is challenging to optimize optical satellite imagery to both interpolate current and extrapolate future forest structure and composition. We identified a need to understand how early spectral dynamics (5 years post‐fire) inform patterns of structural recovery after fire disturbance. To create these structural patterns, we collected metrics of forest structure using high‐density Remotely Piloted Aircraft (RPAS) lidar (light detection and ranging). We employed a space‐for‐time substitution in the highly fire‐disturbed forests of interior British Columbia. In this region, we collected RPAS lidar and corresponding field plot data 5‐, 8‐, 11‐,12‐, and 16‐years postfire to predict structural attributes relevant to management, including the percent bare ground, the proportion of coniferous trees, stem density, and basal area. We compared forest structural attributes with unique early spectral responses, or trajectories, derived from Landsat time series data 5 years after fire. A total of eight unique spectral recovery trajectories were identified from spectral responses of seven vegetation indices (NBR, NDMI, NDVI, TCA, TCB, TCG, and TCW) that described five distinct patterns of structural recovery captured with RPAS lidar. Two structural patterns covered more than 80% of the study area. Both patterns had strong coniferous regrowth, but one had a higher basal area with more bare ground and the other pattern had a high stem density, but a low basal area and a higher deciduous proportion. Our approach highlights the ability to use early spectral responses to capture unique spectral trajectories and their associated distinct structural recovery patterns.

中文翻译：

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早期光谱动态表明野火后森林的独特生长模式


最近，北美西部地区的大规模且往往严重程度很高的野火急剧增加。森林火灾后，了解结构恢复模式非常重要，因为恢复模式会影响关键的生态系统服务。卫星观测提供的连续森林监测特别有利于捕捉森林恢复开始的关键火灾后时期。然而，优化光学卫星图像以插值当前并推断未来的森林结构和组成是一项挑战。我们发现需要了解早期光谱动力学（火灾后 5 年）如何影响火灾扰动后的结构恢复模式。为了创建这些结构模式，我们使用高密度遥控飞机 (RPAS) 激光雷达（光检测和测距）收集了森林结构的指标。我们在不列颠哥伦比亚省内陆受火灾高度干扰的森林中采用了时空替代法。在该地区，我们收集了 RPAS 激光雷达和火灾后 5 年、8 年、11 年、12 年和 16 年的相应现场图数据，以预测与管理相关的结构属性，包括裸地百分比、针叶树比例、茎密度和断面积。我们将森林结构属性与独特的早期光谱响应或轨迹进行了比较，这些响应或轨迹源自火灾后 5 年的陆地卫星时间序列数据。从七个植被指数（NBR、NDMI、NDVI、TCA、TCB、TCG 和 TCW）的光谱响应中确定了总共八个独特的光谱恢复轨迹，这些指数描述了 RPAS 激光雷达捕获的五种不同的结构恢复模式。两种构造模式覆盖了研究区域80%以上。 两种模式均具有较强的针叶再生能力，但一种模式的断面积较高，裸地较多；另一种模式的茎密度较高，但断面积较低，落叶比例较高。我们的方法强调了使用早期光谱响应来捕获独特的光谱轨迹及其相关的独特结构恢复模式的能力。