Annual Maps of Forest Harvest Events in Maine from LANDSAT Imagery 1986-2019

We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.

本研究采用陆地卫星（Landsat）遥感影像与森林样地清查数据，针对美国东北部缅因州1986至2019年的潜在森林采伐事件，构建了年度时间序列地图集。首先针对三种不同光谱指数生成了LandTrendr时间分割结果集；对变化检测结果进行滤波处理，移除持续时长超过两年的事件；随后采用七参数退化决策树模型对结果进行融合，该模型基于扰动斑块面积、光谱变化幅度以及多指数间的变化"votes"确定一系列阈值。本研究可检测出总断面积至少减少30%的采伐事件，平均F1分数为0.72（标准差σ=0.02），平均漏检率（虚阴性错误率）为0.32（σ=0.02），平均误检率（虚阳性错误率）为0.23（σ=0.03）；若基于国家土地覆盖数据集（National Land Cover Dataset）和JRC全球地表水分类（JRC Global Surface Water）结果对地图进行掩膜，去除人工土地利用（建成区与农用地）及水体区域，模型性能可进一步提升，平均F1分数达0.73（σ=0.02）。通过与样本外参考数据集及现有国家级森林扰动数据集进行对比验证，结果表明本研究生成的森林采伐地图可准确表征缅因州北部工业林长期采伐格局的时空异质性，是具备区域适用性的可靠数据源。本数据集包含以下内容：潜在森林采伐事件的年度集成地图、基于森林清查参考子集的交叉验证结果（可反映不同子集间的检测一致性），以及可用于掩膜城镇、农用地与水体区域虚假检测结果的辅助数据集。