Knowledge of forest change type and timing is required for forest management, reporting, and science. Time series of historic satellite data (e.g. Landsat) have resulted in an invaluable record of changes in forest conditions. Natural resource management and reporting typically operate at an annual time step, yet the recent addition of data streams from compatible satellites (e.g., Sentinel-2) offer the possibility of generating frequent, management-relevant forest status assessments and maps of change. Analytical approaches that rely on a time series of observations to identify change often struggle to provide reliable estimates of change events in terminal years of the time series until subsequent, additional observations are available. Methods to meaningfully integrate observations from compatible satellite platforms can provide short-term information to augment and refine estimates of change area and type in those terminal years of the time series. In this research we fuse Landsat-8 and Sentinel-2A and -2B data streams to capture, with reduced latency, stand replacing forest change (harvest and wildfire), tagged to a temporal window of occurrence over an ~10,000 km² area of central British Columbia, Canada. We introduce a new algorithm, SLIMS (Shrinking Latency in Multiple Streams), to rapidly and reliably detect change, and then use an established Bayesian approach to meaningfully combine changes detected in the Landsat and Sentinel data streams. Our results indicate that the type and timing of stand-replacing disturbances can be identified in these forests with high accuracy. Overall, 13.9% of the study area was disturbed between the end of 2016 and the end of 2017, with the majority of disturbed area attributable to wildfire and a smaller amount attributed to forest harvesting, mostly in the winter 2016–2017 with some limited summer harvest also occurring. Overall accuracy of the change, assessed using independent validation data, was 95% ± 2.3%. The capacity of these change results to augment a trend-based assessment of change for 2017 was also demonstrated and provides a framework for how short- and long-term change detection approaches provide complementary information that can increase the timeliness and accuracy of change area estimates in the terminal years of a time series. These findings also demonstrate the capacity to regard Landsat and Sentinel-2 sensors as elements of a virtual constellation to obtain forest change information in a timely (i.e., end of growing season) and reliable fashion over large areas.

森林管理、报告和科学需要了解森林变化类型和时间。历史卫星数据（例如 Landsat）的时间序列产生了对森林条件变化的宝贵记录。自然资源管理和报告通常在每年的时间步长运行，但最近增加的来自兼容卫星（例如 Sentinel-2）的数据流提供了生成频繁的、与管理相关的森林状况评估和变化地图的可能性。依赖时间序列观察来识别变化的分析方法通常难以提供时间序列最终年份的变化事件的可靠估计，直到随后的额外观察可用。有意义地整合来自兼容卫星平台的观测的方法可以提供短期信息，以增加和完善对时间序列终端年份中变化区域和类型的估计。在这项研究中，我们融合了 Landsat-8 和 Sentinel-2A 和 -2B 数据流，以减少延迟来捕获替代森林变化（收获和野火）的林分，标记为超过约 10,000 公里的时间窗口²加拿大不列颠哥伦比亚省中部地区。我们引入了一种新算法 SLIMS（多流中的收缩延迟）来快速可靠地检测变化，然后使用已建立的贝叶斯方法有意义地结合在 Landsat 和 Sentinel 数据流中检测到的变化。我们的结果表明，可以在这些森林中以高精度识别林分更换干扰的类型和时间。总体而言，在 2016 年底至 2017 年底期间，13.9% 的研究区域受到干扰，大部分干扰区域归因于野火，少量归因于森林采伐，主要发生在 2016-2017 年冬季，夏季有所限制收获也在发生。使用独立验证数据评估的更改的总体准确性为 95% ± 2.3%。还展示了这些变化结果增强 2017 年基于趋势的变化评估的能力，并提供了一个框架，用于说明短期和长期变化检测方法如何提供补充信息，从而提高变化区域估计的及时性和准确性时间序列的终年。这些发现还证明了将 Landsat 和 Sentinel-2 传感器视为虚拟星座元素的能力，以便及时（即，生长季节结束）和可靠的方式在大范围内获取森林变化信息。