Assessment of pingo distribution and morphometry using an IfSAR derived digital surface model, western Arctic Coastal Plain, Northern Alaska

Geomorphology ◽  
2012 ◽  
Vol 138 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Benjamin M. Jones ◽  
Guido Grosse ◽  
Kenneth M. Hinkel ◽  
Christopher D. Arp ◽  
Shane Walker ◽  
...  
Keyword(s):  
Coastal Plain ◽  
Surface Model ◽  
Digital Surface Model ◽  
Arctic Coastal Plain ◽  
Western Arctic ◽  
Northern Alaska

scholarly journals Methods to assess natural and anthropogenic thaw lake drainage on the western Arctic coastal plain of northern Alaska

2007 ◽  
Vol 112 (F2) ◽  
Author(s):  
Kenneth M. Hinkel ◽  
Benjamin M. Jones ◽  
Wendy R. Eisner ◽  
Chris J. Cuomo ◽  
Richard A. Beck ◽  
...  
Keyword(s):  
Coastal Plain ◽  
Arctic Coastal Plain ◽  
Western Arctic ◽  
Lake Drainage ◽  
Northern Alaska

Morphometric and spatial analysis of thaw lakes and drained thaw lake basins in the western Arctic Coastal Plain, Alaska

2005 ◽  
Vol 16 (4) ◽  
pp. 327-341 ◽  
Author(s):  
K. M. Hinkel ◽  
R. C. Frohn ◽  
F. E. Nelson ◽  
W. R. Eisner ◽  
R. A. Beck
Keyword(s):  
Spatial Analysis ◽  
Coastal Plain ◽  
Arctic Coastal Plain ◽  
Western Arctic ◽  
Thaw Lakes ◽  
Lake Basins

scholarly journals Movements and habitat use of loons for assessment of conservation buffer zones in the Arctic Coastal Plain of northern Alaska

2020 ◽  
Vol 22 ◽  
pp. e00980
Author(s):  
Sharon A. Poessel ◽  
Brian D. Uher-Koch ◽  
John M. Pearce ◽  
Joel A. Schmutz ◽  
Autumn-Lynn Harrison ◽  
...  
Keyword(s):  
Habitat Use ◽  
Coastal Plain ◽  
The Arctic ◽  
Buffer Zones ◽  
Arctic Coastal Plain ◽  
Northern Alaska

scholarly journals Remote sensing of lake water volumes on the Arctic Coastal Plain of Northern Alaska

2020 ◽  
Author(s):  
Claire E. Simpson ◽  
Christopher D. Arp ◽  
Yongwei Sheng ◽  
Mark L. Carroll ◽  
Benjamin M. Jones ◽  
...  
Keyword(s):  
Lake Water ◽  
Coastal Plain ◽  
Water Storage ◽  
Regional Model ◽  
The Arctic ◽  
Lake Depth ◽  
Arctic Coastal Plain ◽  
Water Volumes ◽  
Northern Alaska

Abstract. The Pleistocene Sand Sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by an ancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, which cover approximately 20 % of the Pleistocene Sand Sea, are relatively deep (up to 25 m). In addition to the natural importance of ACP Sand Sea lakes for water storage, energy balance, and ecological habitat, the need for winter water for industrial development and exploration activities makes lakes in this region a valuable resource. However, ACP Sand Sea lakes have received little prior study. Here, we use in situ bathymetric data to test 12 model variants for predicting Sand Sea lake depth based on analysis of Landast-8 Operational Land Imager (OLI) images. Lake depth gradients were measured at 17 lakes in mid-summer 2017 using a HumminBird 798ci HD SI Combo automatic sonar system (Simpson and Arp, 2018). The field measured data points were compared to Red-Green-Blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and estimate bathymetry (Simpson, 2019). Exponential functions using a simple band ratio (with bands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For each lake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetries were integrated with remotely sensed lake surface area to quantify lake water storage volumes, which ranged from 1.056 × 10−3 km3 to 57.416 × 10−3 km3. Due to variation in depth maxima, substrate, and turbidity between lakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ data with which to develop a regional model solution. Estimating lake water volumes using remote sensing will facilitate better management of expanding development activities and serve as a baseline by which to evaluate future responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lake bathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) and https://doi.org/10.18739/A2TQ5RD83 (Simpson, 2019), respectively.

scholarly journals Landsat-derived bathymetry of lakes on the Arctic Coastal Plain of northern Alaska

2021 ◽  
Vol 13 (3) ◽  
pp. 1135-1150
Author(s):  
Claire E. Simpson ◽  
Christopher D. Arp ◽  
Yongwei Sheng ◽  
Mark L. Carroll ◽  
Benjamin M. Jones ◽  
...  
Keyword(s):  
Lake Water ◽  
Coastal Plain ◽  
Water Storage ◽  
Regional Model ◽  
The Arctic ◽  
Landsat 8 ◽  
Lake Depth ◽  
Arctic Coastal Plain ◽  
Northern Alaska

Abstract. The Pleistocene sand sea on the Arctic Coastal Plain (ACP) of northern Alaska is underlain by an ancient sand dune field, a geological feature that affects regional lake characteristics. Many of these lakes, which cover approximately 20 % of the Pleistocene sand sea, are relatively deep (up to 25 m). In addition to the natural importance of ACP sand sea lakes for water storage, energy balance, and ecological habitat, the need for winter water for industrial development and exploration activities makes lakes in this region a valuable resource. However, ACP sand sea lakes have received little prior study. Here, we collect in situ bathymetric data to test 12 model variants for predicting sand sea lake depth based on analysis of Landsat-8 Operational Land Imager (OLI) images. Lake depth gradients were measured at 17 lakes in midsummer 2017 using a Humminbird 798ci HD SI Combo automatic sonar system. The field-measured data points were compared to red–green–blue (RGB) bands of a Landsat-8 OLI image acquired on 8 August 2016 to select and calibrate the most accurate spectral-depth model for each study lake and map bathymetry. Exponential functions using a simple band ratio (with bands selected based on lake turbidity and bed substrate) yielded the most successful model variants. For each lake, the most accurate model explained 81.8 % of the variation in depth, on average. Modeled lake bathymetries were integrated with remotely sensed lake surface area to quantify lake water storage volumes, which ranged from 1.056×10-3 to 57.416×10-3 km3. Due to variations in depth maxima, substrate, and turbidity between lakes, a regional model is currently infeasible, rendering necessary the acquisition of additional in situ data with which to develop a regional model solution. Estimating lake water volumes using remote sensing will facilitate better management of expanding development activities and serve as a baseline by which to evaluate future responses to ongoing and rapid climate change in the Arctic. All sonar depth data and modeled lake bathymetry rasters can be freely accessed at https://doi.org/10.18739/A2SN01440 (Simpson and Arp, 2018) and https://doi.org/10.18739/A2HT2GC6G (Simpson, 2019), respectively.

scholarly journals Spatio-Temporal Analysis of Gyres in Oriented Lakes on the Arctic Coastal Plain of Northern Alaska Based on Remotely Sensed Images

2014 ◽  
Vol 6 (10) ◽  
pp. 9170-9193 ◽  
Author(s):  
Shengan Zhan ◽  
Richard Beck ◽  
Kenneth Hinkel ◽  
Hongxing Liu ◽  
Benjamin Jones
Keyword(s):  
Coastal Plain ◽  
Temporal Analysis ◽  
Remotely Sensed ◽  
The Arctic ◽  
Arctic Coastal Plain ◽  
Remotely Sensed Images ◽  
Spatio Temporal ◽  
Northern Alaska
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