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

Author

Listed:
  • Mariana Verdonen
  • Miguel Villoslada
  • Tiina H. M. Kolari
  • Teemu Tahvanainen
  • Pasi Korpelainen
  • Paolo Tarolli
  • Timo Kumpula

Abstract

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.

Suggested Citation

  • Mariana Verdonen & Miguel Villoslada & Tiina H. M. Kolari & Teemu Tahvanainen & Pasi Korpelainen & Paolo Tarolli & Timo Kumpula, 2025. "Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data," Permafrost and Periglacial Processes, John Wiley & Sons, vol. 36(1), pages 22-36, January.
    • Handle: RePEc:wly:perpro:v:36:y:2025:i:1:p:22-36
    DOI: 10.1002/ppp.2252
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Julia Bosiö & Margareta Johansson & Terry Callaghan & Bernt Johansen & Torben Christensen, 2012. "Future vegetation changes in thawing subarctic mires and implications for greenhouse gas exchange—a regional assessment," Climatic Change, Springer, vol. 115(2), pages 379-398, November.
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