State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China.
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China.
Sci Total Environ. 2022 Feb 10;807(Pt 2):150828. doi: 10.1016/j.scitotenv.2021.150828. Epub 2021 Oct 7.
The rapidly warming Arctic climate is reducing the stability of near-surface permafrost, and the thawing of ice-rich permafrost causes landscape changes known as thermokarst processes. Growing evidence suggests an increasing trend in the frequency and magnitude of thermokarst lake drainage events, which would significantly alter topography and hydrology, affecting ecosystem stability and carbon cycling. Dynamic monitoring of thermokarst lakes through satellite imagery remains a challenging task, as current temporal trend analysis methods have difficulty in accurately detecting when thermokarst lake drainage events occur. In this study, to improve the detection of time series breakpoints, an advanced temporal segmentation and change detection algorithm developed for forest change detection was, for the first time, transposed to monitor thermokarst lake dynamics. Moreover, to filter out spurious signals caused by fluctuations in lake area, we developed a hybrid algorithm to validate the detected thermokarst lake drainage events at the pixel-level and lake object-level, respectively. The method developed in this study demonstrates its effectiveness in detecting thermokarst lake drainage events in Arctic permafrost ecosystems and the potential to monitor the evolution of thermokarst landscapes using Landsat archive. A time-series analysis of changes in the thermokarst lake region of northern Alaska since 2000 using all available Landsat continuous data was performed on the Google Earth Engine platform. In total, 90 drainage lakes larger than 5 ha in size were detected in our study area, nearly a third of which were almost completely drained. As thermokarst lakes drainage represent hotspots of permafrost degradation, we publicly share information on these drained lakes to help select more targeted sites for costly fieldwork and validation activities. This study provides a basis for understanding and quantifying thermokarst lake dynamics in the Arctic permafrost region, which will contribute to the goal of integrating thermokarst processes into earth system models.
快速变暖的北极气候正在降低近地表多年冻土的稳定性，而富冰多年冻土的融化导致了被称为热喀斯特过程的景观变化。越来越多的证据表明，热喀斯特湖排水事件的频率和规模呈上升趋势，这将显著改变地形和水文，影响生态系统的稳定性和碳循环。通过卫星图像对热喀斯特湖进行动态监测仍然是一项具有挑战性的任务，因为当前的时间序列趋势分析方法很难准确检测到热喀斯特湖排水事件的发生时间。在这项研究中，为了提高时间序列断点的检测能力，首次将一种为森林变化检测开发的先进的时间分割和变化检测算法转换为监测热喀斯特湖动态。此外，为了滤除因湖泊面积波动而产生的虚假信号，我们分别开发了一种混合算法来验证检测到的热喀斯特湖排水事件的像素级和湖泊对象级。本研究开发的方法在检测北极多年冻土生态系统中的热喀斯特湖排水事件方面表现出了有效性，并且有可能利用 Landsat 存档监测热喀斯特景观的演变。在 Google Earth Engine 平台上，对 2000 年以来阿拉斯加北部多年冻土区热喀斯特湖地区的变化进行了时间序列分析，利用所有可用的 Landsat 连续数据。在我们的研究区域内，共检测到 90 个大于 5 公顷的排水湖，其中近三分之一几乎完全干涸。由于热喀斯特湖排水是多年冻土退化的热点，我们公开分享这些排水湖的信息，以帮助选择更有针对性的地点进行昂贵的实地工作和验证活动。本研究为理解和量化北极多年冻土区热喀斯特湖动态提供了基础，这将有助于将热喀斯特过程纳入地球系统模型的目标。
