Spatial information about disturbance driven patterns of forest structure and ages across landscapes provide a valuable resource for all land management efforts including cross-ownership collaborative forest treatments and restoration. While disturbance events in general are known to impact stand characteristics, the agent of change may also influence recovery and the supply of ecosystem services. Our study utilizes the full extent of the Landsat archive to identify the timing, extent, magnitude, and agent, of the most recent fast disturbance event for all forested lands within Minnesota, USA. To account for the differences in the Landsat sensors through time, specifically the coarser spatial, spectral, and radiometric resolutions of the early MSS sensors, we employed a two-step approach, first harmonizing spectral indices across the Landsat sensors, then applying a segmentation algorithm to fit temporal trends to the time series to identify abrupt forest disturbance events. We further incorporated spectral, topographic, and land protection information in our classification of the agent of change for all disturbance patches. After allowing two years for the time series to stabilize, we were able to identify the most recent fast disturbance events across Minnesota from 1974–2018 with a change versus no-change validation accuracy of 97.2% ± 1.9%, and higher omission (14.9% ± 9.3%) than commission errors (1.6% ± 1.9%) for the identification of change patches. Our classification of the agent of change exhibited an overall accuracy of 96.5% ± 1.9% with classes including non-disturbed forest, land conversion, fire, flooding, harvest, wind/weather, and other rare natural events. Individual class errors varied, but all class user and producer accuracies were above 78%. The unmatched nature of the Landsat archive for providing comparable forest attribute and change information across more than four decades highlights the value of the totality of the Landsat program to the larger geospatial, ecological research, and forest management communities.

中文翻译：

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美国明尼苏达州森林扰动的四个十年

有关受干扰驱动的森林结构和景观年龄格局的空间信息为所有土地管理工作提供了宝贵资源，包括交叉所有权，合作森林处理和恢复。虽然通常已经知道干扰事件会影响林分特征，但变化的动因也可能影响生态系统服务的恢复和供应。我们的研究利用Landsat档案库的全部范围来确定美国明尼苏达州所有林地的最新快速干扰事件的发生时间，范围，大小和因素。为了解决Landsat传感器随时间的差异，特别是早期MSS传感器的较粗的空间，光谱和辐射分辨率，我们采用了两步法，首先协调Landsat传感器上的光谱指数，然后应用分段算法将时间趋势拟合到时间序列中，以识别突然的森林干扰事件。我们还将光谱，地形和土地保护信息纳入我们对所有扰动斑块的变化动因分类中。经过两年的时间序列稳定后，我们能够确定1974-2018年在明尼苏达州发生的最新快速扰动事件，变更与不变的确认准确度为97.2％±1.9％，而遗漏率更高（14.9％ ±9.3％）以外的佣金错误（1.6％±1.9％）来标识变更补丁。我们对变更代理的分类显示出96.5％±1.9％的整体准确度，其中包括未受干扰的森林，土地转化，火灾，洪水，收获，风/天气和其他罕见的自然事件。各个班级的错误有所不同，但所有班级用户和生产者的准确性均高于78％。Landsat档案馆具有无与伦比的性质，可在过去的40年中提供可比的森林属性和变化信息，这凸显了Landsat计划的整体对更大的地理空间，生态研究和森林管理社区的价值。