Using floristic gradient mapping to assess seasonal thaw depth in interior Alaska
Questions Is it possible to map floristic gradients in heterogeneous boreal vegetation by using remote‐sensing data? Does a continuous vegetation map enable the creation of a spatially continuous map of seasonal permafrost soil thaw depth? Location Bonanza Creek LTER, Fairbanks, Alaska, USA. Methods...
| Main Authors: | , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://depositonce.tu-berlin.de/handle/11303/13300 https://doi.org/10.14279/depositonce-12092 |
| Summary: | Questions Is it possible to map floristic gradients in heterogeneous boreal vegetation by using remote‐sensing data? Does a continuous vegetation map enable the creation of a spatially continuous map of seasonal permafrost soil thaw depth? Location Bonanza Creek LTER, Fairbanks, Alaska, USA. Methods Vegetation records are subjected to an ordination to extract the predominant floristic gradient. The ordination scores are then extrapolated using Sentinel 2 imagery and a digital elevation model (DEM). As the relation between vegetation pattern and seasonal thaw depth was confirmed in this study, the spatial distribution of ordination scores is then used to predict seasonal thaw depth over the same area. Results The first dimension of the ordination space separates species corresponding to moist and cold soil conditions from species associated with well‐drained soils. This floristic gradient was successfully mapped within the sampled plant communities. The extrapolated thaw depths follow the typical distribution along a topographical and geomorphological gradient for this region. Besides vegetation information also DEM derivatives show high contributions to the thaw depth modeling. Conclusion We demonstrate that floristic gradient mapping in boreal vegetation is possible. The accuracy of the thaw depth prediction model is comparable to that in previous analyses but uses a more parsimonious set of predictors, underlining the efficacy of this approach. TU Berlin, Open-Access-Mittel – 2021 |
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