Abstract Landsat time series (LTS) enable the characterization of forest recovery post-disturbance over large areas; however, there is a gap in our current knowledge concerning the linkage between spectral measures of recovery derived from LTS and actual manifestations of forest structure in regenerating stands. Airborne laser scanning (ALS) data provide useful measures of forest structure that can be used to corroborate spectral measures of forest recovery. The objective of this study was to evaluate the utility of a spectral index of recovery based on the Normalized Burn Ratio (NBR): the years to recovery, or Y2R metric, as an indicator of the return of forest vegetation following forest harvest (clearcutting). The Y2R metric has previously been defined as the number of years required for a pixel to return to 80% of its pre-disturbance NBR (NBRpre) value. In this study, the Composite2Change (C2C) algorithm was used to generate a time series of gap-free, cloud-free Landsat surface reflectance composites (1985–2012), associated change metrics, and a spatially-explicit dataset of detected changes for an actively managed forest area in southern Finland (5.3 Mha). The overall accuracy of change detection, determined using independent validation data, was 89%. Areas of forest harvesting in 1991 were then used to evaluate the Y2R metric. Four alternative recovery scenarios were evaluated, representing variations in the spectral threshold used to define Y2R: 60%, 80%, and 100% of NBRpre, and a critical value of z (i.e. the year in which the pixel's NBR value is no longer significantly different from NBRpre). The Y2R for each scenario were classified into five groups: recovery within 17 years, and not recovered. Measures of forest structure (canopy height and cover) were obtained from ALS data. Benchmarks for height (>5 m) and canopy cover (>10%) were applied to each recovery scenario, and the percentage of pixels that attained both of these benchmarks for each recovery group, was determined for each Y2R scenario. Our results indicated that the Y2R metric using the 80% threshold provided the most realistic assessment of forest recovery: all pixels considered in our analysis were spectrally recovered within the analysis period, with 88.88% of recovered pixels attaining the benchmarks for both cover and height. Moreover, false positives (pixels that had recovered spectrally, but not structurally) and false negatives (pixels that had recovered structurally, but not spectrally) were minimized with the 80% threshold. This research demonstrates the efficacy of LTS-derived assessments of recovery, which can be spatially exhaustive and retrospective, providing important baseline data for forest monitoring.

中文翻译：

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使用来自机载激光扫描数据的森林覆盖率和高度测量值确认来自 Landsat 时间序列的收获后光谱恢复

摘要 Landsat 时间序列 (LTS) 能够表征大面积受干扰后的森林恢复；然而，我们目前关于从 LTS 得出的恢复光谱测量与再生林中森林结构的实际表现之间的联系的知识存在差距。机载激光扫描 (ALS) 数据提供了有用的森林结构测量，可用于证实森林恢复的光谱测量。本研究的目的是评估基于归一化燃烧率 (NBR) 的光谱恢复指数的效用：恢复年数或 Y2R 指标，作为森林收获（砍伐）后森林植被恢复的指标. Y2R 指标以前被定义为像素恢复到其扰动前 NBR (NBRpre) 值的 80% 所需的年数。在本研究中，Composite2Change (C2C) 算法用于生成无间隙、无云的 Landsat 表面反射复合材料（1985-2012）的时间序列、相关的变化指标以及检测到的变化的空间明确数据集芬兰南部积极管理的森林面积（5.3 Mha）。使用独立验证数据确定的变更检测的整体准确度为 89%。然后使用 1991 年的森林采伐面积来评估 Y2R 指标。评估了四种替代恢复方案，代表用于定义 Y2R 的光谱阈值的变化：NBRpre 的 60%、80% 和 100%，以及 z 的临界值（即像素所在的年份）s NBR 值不再与 NBRpre 显着不同）。每种情景的 Y2R 分为五组：17 年内恢复和未恢复。森林结构的测量（树冠高度和覆盖率）是从 ALS 数据中获得的。高度 (>5 m) 和树冠覆盖 (>10%) 的基准被应用于每个恢复场景，并且每个恢复组达到这两个基准的像素百分比是为每个 Y2R 场景确定的。我们的结果表明，使用 80% 阈值的 Y2R 指标提供了最真实的森林恢复评估：我们分析中考虑的所有像素都在分析期内进行了光谱恢复，88.88% 的恢复像素达到了覆盖率和高度的基准。此外，误报（已恢复光谱的像素，但不是结构上）和假阴性（已在结构上恢复但未在光谱上恢复的像素）使用 80% 阈值最小化。这项研究证明了 LTS 衍生的恢复评估的有效性，它可以是空间详尽的和回顾性的，为森林监测提供重要的基线数据。