Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data

Maximum seasonal thaw depth, referred to as active layer thickness (ALT), is one of the key parameters used to monitor permafrost conditions. ALT maps based on interpolation of point measurements or derived from coarse or moderate spatial resolution satellite data often hide small-scale spatial vari...

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Published in:Permafrost and Periglacial Processes
Main Authors: Verdonen M., Villoslada M., Kolari T., Tahvanainen T., Korpelainen P., Tarolli P., Kumpula T.
Other Authors: Verdonen, M., Villoslada, M., Kolari, T., Tahvanainen, T., Korpelainen, P., Tarolli, P., Kumpula, T.
Format: Article in Journal/Newspaper
Language:English
Published: John Wiley and Sons Ltd 2025
Subjects:
Online Access:https://hdl.handle.net/11577/3548432
https://doi.org/10.1002/ppp.2252
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author Verdonen M.
Villoslada M.
Kolari T.
Tahvanainen T.
Korpelainen P.
Tarolli P.
Kumpula T.
author2 Verdonen, M.
Villoslada, M.
Kolari, T.
Tahvanainen, T.
Korpelainen, P.
Tarolli, P.
Kumpula, T.
author_facet Verdonen M.
Villoslada M.
Kolari T.
Tahvanainen T.
Korpelainen P.
Tarolli P.
Kumpula T.
author_sort Verdonen M.
collection Padua Research Archive (IRIS - Università degli Studi di Padova)
container_issue 1
container_start_page 22
container_title Permafrost and Periglacial Processes
container_volume 36
description Maximum seasonal thaw depth, referred to as active layer thickness (ALT), is one of the key parameters used to monitor permafrost conditions. ALT maps based on interpolation of point measurements or derived from coarse or moderate spatial resolution satellite data often hide small-scale spatial variations in thaw depth resulting from differences in surface characteristics and microtopography. To model and predict changes in hydrological and biogeochemical processes in permafrost areas accurately, high-resolution remote sensing-based estimations of ALT are needed. Therefore, we applied random forest (RF) regression on a set of topographical and spectral vegetation indices derived from optical unoccupied aerial systems data, Landsat 8 land surface temperature (LST) data, and field measurements to estimate thaw depths in palsas at three mires in north-west Finland. We also analyzed differences in thaw depths between mires located at different elevations, between dome and plateau-shaped palsas, and between different vegetation and surface cover classes. The RF models resulted in root mean square errors from 2.4 to 5.7 cm between predicted and observed thaw depths and the R2 values of 0.57–0.96. Height from the surrounding fen surface and LST were the most important variables in thaw depth models, although high-accuracy results were also achieved without LST. The mean thaw depths did not differ between the sites with lowest and highest elevation, whereas the thaw depths were significantly deeper in dome-shaped palsas compared to plateaus. The thaw depths were significantly different between vegetation cover classes only on plateau-shaped palsas. The results indicate the high impact of the topography on the palsa thaw depth, thus highlighting the importance of accurate elevation models in spatial modeling of palsa ALT. The methodology presented in this study can be applied to other permafrost regions where field measurements of ALT are accompanied with high-resolution topographical and multispectral data.
format Article in Journal/Newspaper
genre Active layer thickness
palsa
palsas
Peat
Peat plateau
permafrost
genre_facet Active layer thickness
palsa
palsas
Peat
Peat plateau
permafrost
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op_doi https://doi.org/10.1002/ppp.2252
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volume:36
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numberofpages:15
journal:PERMAFROST AND PERIGLACIAL PROCESSES
https://hdl.handle.net/11577/3548432
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spelling ftunivpadovairis:oai:www.research.unipd.it:11577/3548432 2025-05-04T14:06:11+00:00 Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data Verdonen M. Villoslada M. Kolari T. Tahvanainen T. Korpelainen P. Tarolli P. Kumpula T. Verdonen, M. Villoslada, M. Kolari, T. Tahvanainen, T. Korpelainen, P. Tarolli, P. Kumpula, T. 2025 ELETTRONICO https://hdl.handle.net/11577/3548432 https://doi.org/10.1002/ppp.2252 eng eng John Wiley and Sons Ltd info:eu-repo/semantics/altIdentifier/wos/WOS:001314302300001 volume:36 issue:1 firstpage:22 lastpage:36 numberofpages:15 journal:PERMAFROST AND PERIGLACIAL PROCESSES https://hdl.handle.net/11577/3548432 doi:10.1002/ppp.2252 info:eu-repo/semantics/openAccess active layer palsa peat plateau random forest remote sensing UAS info:eu-repo/semantics/article 2025 ftunivpadovairis https://doi.org/10.1002/ppp.2252 2025-04-10T14:14:19Z Maximum seasonal thaw depth, referred to as active layer thickness (ALT), is one of the key parameters used to monitor permafrost conditions. ALT maps based on interpolation of point measurements or derived from coarse or moderate spatial resolution satellite data often hide small-scale spatial variations in thaw depth resulting from differences in surface characteristics and microtopography. To model and predict changes in hydrological and biogeochemical processes in permafrost areas accurately, high-resolution remote sensing-based estimations of ALT are needed. Therefore, we applied random forest (RF) regression on a set of topographical and spectral vegetation indices derived from optical unoccupied aerial systems data, Landsat 8 land surface temperature (LST) data, and field measurements to estimate thaw depths in palsas at three mires in north-west Finland. We also analyzed differences in thaw depths between mires located at different elevations, between dome and plateau-shaped palsas, and between different vegetation and surface cover classes. The RF models resulted in root mean square errors from 2.4 to 5.7 cm between predicted and observed thaw depths and the R2 values of 0.57–0.96. Height from the surrounding fen surface and LST were the most important variables in thaw depth models, although high-accuracy results were also achieved without LST. The mean thaw depths did not differ between the sites with lowest and highest elevation, whereas the thaw depths were significantly deeper in dome-shaped palsas compared to plateaus. The thaw depths were significantly different between vegetation cover classes only on plateau-shaped palsas. The results indicate the high impact of the topography on the palsa thaw depth, thus highlighting the importance of accurate elevation models in spatial modeling of palsa ALT. The methodology presented in this study can be applied to other permafrost regions where field measurements of ALT are accompanied with high-resolution topographical and multispectral data. Article in Journal/Newspaper Active layer thickness palsa palsas Peat Peat plateau permafrost Padua Research Archive (IRIS - Università degli Studi di Padova) Permafrost and Periglacial Processes 36 1 22 36
spellingShingle active layer
palsa
peat plateau
random forest
remote sensing
UAS
Verdonen M.
Villoslada M.
Kolari T.
Tahvanainen T.
Korpelainen P.
Tarolli P.
Kumpula T.
Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data
title Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data
title_full Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data
title_fullStr Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data
title_full_unstemmed Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data
title_short Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data
title_sort spatial distribution of thaw depth in palsas estimated from optical unoccupied aerial systems data
topic active layer
palsa
peat plateau
random forest
remote sensing
UAS
topic_facet active layer
palsa
peat plateau
random forest
remote sensing
UAS
url https://hdl.handle.net/11577/3548432
https://doi.org/10.1002/ppp.2252