Abstract
Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.
Highlights
The high-latitude regions of Eurasia are warming at approximately 0.12 ◦C per year, significantly faster than the global average (e.g., [1,2,3])
Environmental changes associated with a warming climate have significant impacts on arctic and subarctic ecosystems, including surface and subsurface hydrology
We tested and applied the method to analyze 32 years of spatial changes in vegetation and surface hydrology for an area of more than 60,000 km2 in the Lower Yenisei River region. The goal of this mapping effort was to relate land cover changes to underlying permafrost conditions, with the direct connection between permafrost continuity, depth, and age and overlying vegetation previously established in this region by Tyrtikov [44], Rodionov et al [45], and most recently by Streleskiy et al [29]
Summary
The high-latitude regions of Eurasia are warming at approximately 0.12 ◦C per year, significantly faster than the global average (e.g., [1,2,3]). Environmental changes associated with a warming climate have significant impacts on arctic and subarctic ecosystems, including surface and subsurface hydrology. Increased photosynthetic productivity under warming climatic conditions, derived from remotely sensed normalized difference vegetation index (NDVI) data, frequently referred to as “arctic greening,” has been reported for several Eurasian arctic regions (e.g., [4,5,6,7,8]). One of the major drivers of the observed greening trend is the increased abundance of shrub species in tundra ecosystems (e.g., [9,10,11,12,13]). Several studies have detected shifts in treelines both northward and at higher elevations in arctic and subarctic regions (e.g., [19,20,21,22,23])
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