Next Article in Journal
Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas
Next Article in Special Issue
Drivers of Landscape Changes in Coastal Ecosystems on the Yukon-Kuskokwim Delta, Alaska
Previous Article in Journal
Spatio-Temporal Variations of Soil Active Layer Thickness in Chinese Boreal Forests from 2000 to 2015
Previous Article in Special Issue
Sentinel-1 InSAR Measurements of Elevation Changes over Yedoma Uplands on Sobo-Sise Island, Lena Delta
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(8), 1226; https://doi.org/10.3390/rs10081226

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.
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)
Full-Text   |   PDF [3801 KB, uploaded 4 August 2018]   |  

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. 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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top