Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques

Groundwater icings, typical features of permafrost hydrology, are indicative of hydrothermal interactions between surface and ground waters, and permafrost. Their main morphological parameters, i.e., icing area and volume, are generally estimated with low accuracy. Only scarce field observational da...

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Published in:Remote Sensing
Main Authors: Leonid Gagarin, Qingbai Wu, Andrey Melnikov, Nataliya Volgusheva, Nikita Tananaev, Huijun Jin, Ze Zhang, Vladimir Zhizhin
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
Q
Ice
Online Access:https://doi.org/10.3390/rs12040692
https://doaj.org/article/ddba7dbb20134ecf97dfd44049191dde
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spelling ftdoajarticles:oai:doaj.org/article:ddba7dbb20134ecf97dfd44049191dde 2023-05-15T16:37:34+02:00 Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques Leonid Gagarin Qingbai Wu Andrey Melnikov Nataliya Volgusheva Nikita Tananaev Huijun Jin Ze Zhang Vladimir Zhizhin 2020-02-01T00:00:00Z https://doi.org/10.3390/rs12040692 https://doaj.org/article/ddba7dbb20134ecf97dfd44049191dde EN eng MDPI AG https://www.mdpi.com/2072-4292/12/4/692 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs12040692 https://doaj.org/article/ddba7dbb20134ecf97dfd44049191dde Remote Sensing, Vol 12, Iss 4, p 692 (2020) groundwater icing sub-permafrost groundwater supra-permafrost groundwater remote sensing uav-based photogrammetry sokolov equations of icing morphometry southern yakutia Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12040692 2022-12-31T16:19:45Z Groundwater icings, typical features of permafrost hydrology, are indicative of hydrothermal interactions between surface and ground waters, and permafrost. Their main morphological parameters, i.e., icing area and volume, are generally estimated with low accuracy. Only scarce field observational data on icing volume and seasonal development exist to date. Our study evaluates and compares performance of several widely used techniques of icing morphometric estimation, based on field data, collected on a giant Icing #2 in the Samokit River basin, southern Yakutia. Groundwater icing area was estimated by: (a) staking, (b) unmanned aerial vehicle (UAV) surveys, and (c) satellite imagery analysis. Icing #2 area in late February was between 1.38·10 6 m 2 and 1.68·10 6 m 2 , icing volume, between 1.73·10 6 m 3 and 4.20·10 6 m 3 , depending on the technique used. Staking is the least accurate, but also the only direct technique, which is hence used as a baseline tool in our study. Staking-based assessment of icing morphometry is the most conservative, while UAV-based estimates of icing area are higher by 14% to 17%, and of icing volume, by 74% to 142%, compared to staking. The latter appears, in our case, to be the least accurate method, although a direct one. It requires a sufficient number of staking points and transects, which should be set up to represent all icing zones, i.e., channel branches and alluvial islands. Photogrammetry based on UAV surveys has numerous advantages, i.e., higher precision of a per pixel icing volume calculation, based on an ice-free valley bottom digital surface model (DSM), and potential reusability of a resulting DSM. However, positioning precision suffers from the overlay of multiple flyovers required because of battery replacements, and, in our case, an insufficient number of ground control points. Satellite imagery along with B.L. Sokolov’s empirical approach were used to estimate the annual maximum icing area and volume, and the empirical estimates tend to converge to satellite-based ... Article in Journal/Newspaper Ice permafrost Yakutia Directory of Open Access Journals: DOAJ Articles Remote Sensing 12 4 692
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic groundwater icing
sub-permafrost groundwater
supra-permafrost groundwater
remote sensing
uav-based photogrammetry
sokolov equations of icing morphometry
southern yakutia
Science
Q
spellingShingle groundwater icing
sub-permafrost groundwater
supra-permafrost groundwater
remote sensing
uav-based photogrammetry
sokolov equations of icing morphometry
southern yakutia
Science
Q
Leonid Gagarin
Qingbai Wu
Andrey Melnikov
Nataliya Volgusheva
Nikita Tananaev
Huijun Jin
Ze Zhang
Vladimir Zhizhin
Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques
topic_facet groundwater icing
sub-permafrost groundwater
supra-permafrost groundwater
remote sensing
uav-based photogrammetry
sokolov equations of icing morphometry
southern yakutia
Science
Q
description Groundwater icings, typical features of permafrost hydrology, are indicative of hydrothermal interactions between surface and ground waters, and permafrost. Their main morphological parameters, i.e., icing area and volume, are generally estimated with low accuracy. Only scarce field observational data on icing volume and seasonal development exist to date. Our study evaluates and compares performance of several widely used techniques of icing morphometric estimation, based on field data, collected on a giant Icing #2 in the Samokit River basin, southern Yakutia. Groundwater icing area was estimated by: (a) staking, (b) unmanned aerial vehicle (UAV) surveys, and (c) satellite imagery analysis. Icing #2 area in late February was between 1.38·10 6 m 2 and 1.68·10 6 m 2 , icing volume, between 1.73·10 6 m 3 and 4.20·10 6 m 3 , depending on the technique used. Staking is the least accurate, but also the only direct technique, which is hence used as a baseline tool in our study. Staking-based assessment of icing morphometry is the most conservative, while UAV-based estimates of icing area are higher by 14% to 17%, and of icing volume, by 74% to 142%, compared to staking. The latter appears, in our case, to be the least accurate method, although a direct one. It requires a sufficient number of staking points and transects, which should be set up to represent all icing zones, i.e., channel branches and alluvial islands. Photogrammetry based on UAV surveys has numerous advantages, i.e., higher precision of a per pixel icing volume calculation, based on an ice-free valley bottom digital surface model (DSM), and potential reusability of a resulting DSM. However, positioning precision suffers from the overlay of multiple flyovers required because of battery replacements, and, in our case, an insufficient number of ground control points. Satellite imagery along with B.L. Sokolov’s empirical approach were used to estimate the annual maximum icing area and volume, and the empirical estimates tend to converge to satellite-based ...
format Article in Journal/Newspaper
author Leonid Gagarin
Qingbai Wu
Andrey Melnikov
Nataliya Volgusheva
Nikita Tananaev
Huijun Jin
Ze Zhang
Vladimir Zhizhin
author_facet Leonid Gagarin
Qingbai Wu
Andrey Melnikov
Nataliya Volgusheva
Nikita Tananaev
Huijun Jin
Ze Zhang
Vladimir Zhizhin
author_sort Leonid Gagarin
title Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques
title_short Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques
title_full Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques
title_fullStr Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques
title_full_unstemmed Morphometric Analysis of Groundwater Icings: Intercomparison of Estimation Techniques
title_sort morphometric analysis of groundwater icings: intercomparison of estimation techniques
publisher MDPI AG
publishDate 2020
url https://doi.org/10.3390/rs12040692
https://doaj.org/article/ddba7dbb20134ecf97dfd44049191dde
genre Ice
permafrost
Yakutia
genre_facet Ice
permafrost
Yakutia
op_source Remote Sensing, Vol 12, Iss 4, p 692 (2020)
op_relation https://www.mdpi.com/2072-4292/12/4/692
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs12040692
https://doaj.org/article/ddba7dbb20134ecf97dfd44049191dde
op_doi https://doi.org/10.3390/rs12040692
container_title Remote Sensing
container_volume 12
container_issue 4
container_start_page 692
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