Next Article in Journal
Phenocams Bridge the Gap between Field and Satellite Observations in an Arid Grassland Ecosystem
Next Article in Special Issue
A Phenological Approach to Spectral Differentiation of Low-Arctic Tundra Vegetation Communities, North Slope, Alaska
Previous Article in Journal
Assessment of the Hydro-Ecological Impacts of the Three Gorges Dam on China’s Largest Freshwater Lake
Previous Article in Special Issue
Regional Quantitative Cover Mapping of Tundra Plant Functional Types in Arctic Alaska
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(10), 1072; doi:10.3390/rs9101072

The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images

1
Department of Physics, University of Dayton, Dayton, OH 45469, USA
2
Department of Electrical & Computer Engineering, University of Dayton, Dayton, OH 45469, USA
3
School of Environment, Earth and Ecosystem Sciences, The Open University, Milton Keynes MK7 6AA, UK
*
Author to whom correspondence should be addressed.
Received: 9 September 2017 / Revised: 10 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
(This article belongs to the Special Issue Remote Sensing of Arctic Tundra)
View Full-Text   |   Download PDF [4483 KB, uploaded 6 November 2017]   |  

Abstract

The climate of the Arctic is warming rapidly and this is causing major changes to the cycling of carbon and the distribution of permafrost in this region. Tundra lakes are key components of the Arctic climate system because they represent a source of methane to the atmosphere. In this paper, we aim to analyze the geometry of the patterns formed by large (> 0.8 km 2 ) tundra lakes in the Russian High Arctic. We have studied images of tundra lakes in historical maps from the State Hydrological Institute, Russia (date 1977; scale 0.21166 km/pixel) and in Landsat satellite images derived from the Google Earth Engine (G.E.E.; date 2016; scale 0.1503 km/pixel). The G.E.E. is a cloud-based platform for planetary-scale geospatial analysis on over four decades of Landsat data. We developed an image-processing algorithm to segment these maps and images, measure the area and perimeter of each lake, and compute the fractal dimension of the lakes in the images we have studied. Our results indicate that as lake size increases, their fractal dimension bifurcates. For lakes observed in historical maps, this bifurcation occurs among lakes larger than 100 km 2 (fractal dimension 1.43 to 1.87 ). For lakes observed in satellite images this bifurcation occurs among lakes larger than ∼100 km 2 (fractal dimension 1.31 to 1.95 ). Tundra lakes with a fractal dimension close to 2 have a tendency to be self-similar with respect to their area–perimeter relationships. Area–perimeter measurements indicate that lakes with a length scale greater than 70 km 2 are power-law distributed. Preliminary analysis of changes in lake size over time in paired lakes (lakes that were visually matched in both the historical map and the satellite imagery) indicate that some lakes in our study region have increased in size over time, whereas others have decreased in size over time. Lake size change during this 39-year time interval can be up to half the size of the lake as recorded in the historical map. View Full-Text
Keywords: Arctic; permafrost; tundra lakes; image processing; fractals; power law Arctic; permafrost; tundra lakes; image processing; fractals; power law
Figures

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).

Share & Cite This Article

MDPI and ACS Style

Sudakov, I.; Essa, A.; Mander, L.; Gong, M.; Kariyawasam, T. The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images. Remote Sens. 2017, 9, 1072.

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