Effects of Thermokarst Lake Drainage on Localized Vegetation Greening in the Yamal–Gydan Tundra Ecoregion

• Published in: Remote Sensing
• Main Authors: Aobo Liu, Yating Chen, Xiao Cheng
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: Arctic greening; drained lake basins; permafrost; Landsat; machine learning; Yamal–Gydan tundra ecoregion; Science; Q; Arctic Greening; Arctic; Thermokarst; Tundra; Siberia
• Online Access: https://doi.org/10.3390/rs15184561; https://doaj.org/article/5ec98faaff104d2d9c33de7108904089

As the climate warms, the Arctic permafrost region has undergone widespread vegetation changes, exhibiting overall greening trends but with spatial heterogeneity. This study investigates an underexamined mechanism driving heterogeneous greening patterns, thermokarst lake drainage, which creates drained lake basins (DLBs) that represent localized greening hotspots. Focusing on the Yamal–Gydan region in Siberia, we detect 2712 lakes that have drained during the period of 2000–2020, using Landsat time-series imagery and an automated change detection algorithm. Vegetation changes in the DLBs and the entire study area were quantified through NDVI trend analysis. Additionally, a machine learning model was employed to correlate NDVI trajectories in the DLBs with environmental drivers. We find that DLBs provide ideal conditions for plant colonization, with greenness levels reaching or exceeding those of the surrounding vegetation within about five years. The greening trend in DLBs is 8.4 times the regional average, thus contributing disproportionately despite their small area share. Number of years since lake drainage, annual soil temperature, latitude, air temperature trends, and summer precipitation emerged as key factors influencing DLB greening. Our study highlights lake drainage and subsequent vegetation growth as an important fine-scale process augmenting regional greening signals. Quantifying these dynamics is critical for assessing climate impacts on regional vegetation change.