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Open AccessArticle

Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine

1
Department of Geography, Michigan State University, East Lansing, MI, 48823 USA
2
Department of Geography, The George Washington University, Washington, DC, 20052 USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1226; https://doi.org/10.3390/rs10081226
Received: 29 June 2018 / Revised: 20 July 2018 / Accepted: 2 August 2018 / Published: 4 August 2018
(This article belongs to the Special Issue Remote Sensing of Dynamic Permafrost Regions)
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. View Full-Text
Keywords: Landsat dense stacking; Google Earth Engine; climate change; land cover change; permafrost change; Siberia Landsat dense stacking; Google Earth Engine; climate change; land cover change; permafrost change; Siberia
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MDPI and ACS Style

E. Nyland, K.; E. Gunn, G.; I. Shiklomanov, N.; N. Engstrom, R.; A. Streletskiy, D. Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine. Remote Sens. 2018, 10, 1226. https://doi.org/10.3390/rs10081226

AMA Style

E. Nyland K, E. Gunn G, I. Shiklomanov N, N. Engstrom R, A. Streletskiy D. Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine. Remote Sensing. 2018; 10(8):1226. https://doi.org/10.3390/rs10081226

Chicago/Turabian Style

E. Nyland, Kelsey; E. Gunn, Grant; I. Shiklomanov, Nikolay; N. Engstrom, Ryan; A. Streletskiy, Dmitry. 2018. "Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine" Remote Sens. 10, no. 8: 1226. https://doi.org/10.3390/rs10081226

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