Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions

Abstract

Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (−0.69%), Western Alaska (−2.82%), and Kolyma Lowland (−0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.

Monitoring Recent Fluctuations of the Southern Pool of Lake Chad Using Multiple Remote Sensing Data: Implications for Water Balance Analysis

“…The vivid spectral information provided by Landsat TM/ETM+ images has been widely used for mapping water surface area [38][39][40]. The basic logic of these studies is to highlight the difference between water bodies and other objects through the calculation of water indexes [48].…”

“…Landsat TM/ETM+ images, because of their high spatial resolution (30 m), have been widely used to monitor the variations of water surface area [38][39][40]. In total, 904 Landsat TM/ETM+ images from 1991 to 2016 were obtained from the USGS Glovis data archive (http://glovis.usgs.gov).…”

Surface Water Dynamics in the North America Arctic Based on 2000–2016 Landsat Data

“…Some studies observed significant decreases in size and number, and some found increases in the number, as well as in the area of lakes [9,28,29]. Obviously, the results are ambiguous and the reasons for the discrepancies are unclear.…”

“…Previous studies [9,10,13] have indicated that the NDWI [16], normalized difference vegetation index (NDVI) [17], and normalized difference moisture index (NDMI) [18] can be applied to regional-scale water detection, showing high performance in distinguishing water at different band ranges. The modified normalized-Difference Water Index (MNDWI) [19], which has been widely used to detect water, can differentiate water from soil and buildings.…”

“…Some recent studies used Landsat imagery to map water bodies at a finer spatial resolution [6][7][8][9][10], which can detect water bodies less than 0.01 km 2 and provide more details to characterize the inland water status and dynamics. However, nearly all existing Landsat-derived regional to global inundation Water 2018, 10, 824 2 of 12 products are extremely limited by the temporal resolution as they were created only once or every ten years [11,12].…”