Age constraints of rock glaciers in the Southern Alps/New Zealand – Exploring their palaeoclimatic potential

Winkler, Stefan

doi:10.1177/0959683618756802

Cited by 39 publications

(29 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies in Jotunheimen (e.g. Matthews et al, 2014Matthews et al, , 2018Matthews et al, , 2019 and elsewhere (Winkler, 2014;Tomkins et al, 2018a,b;Winkler and Lambiel, 2018) reached similar conclusions for Holocene and Late Glacial timescales and for comparable resistant types of bedrock. Based on at least one 'old' and one 'young' control point with independent age information, the SHD-calibration equation follows the standard equation for linear regression:…”

Section: Schmidt-hammer Exposure-age Dating (Shd)supporting

confidence: 70%

Schmidt-hammer exposure-age dating (SHD) of sorted stripes on Juvflye, Jotunheimen (central South Norway): Morphodynamic and palaeoclimatic implications

et al. 2020

Self Cite

View full text Add to dashboard Cite

Measurements with an electronic Schmidt-hammer (RockSchmidt) were conducted on 23 sites of sorted stripes (periglacial patterned ground) on Juvflye, Jotunheimen (central South Norway). All were located above the current lower limit of alpine permafrost. Performing Schmidt-hammer exposure-age dating (SHD) based on application of a new local age-calibration equation for RRockvalues yielded SHD-ages between 7,975 ± 370 and 6,660 ± 355 years ago, which are closely comparable to results obtained previously from sorted circles at the same location. The age estimates are interpreted as 'composite' ages indicative of upfreezing of boulders, lateral sorting, and subsequent stabilisation. Formation of patterned ground essentially ceased with the onset of the regional Holocene Thermal Maximum (HTM). Neither sorted stripe sites at higher altitude, continuously underlain by permafrost during the entire Holocene, nor those at lower altitudes affected by re-aggradation of permafrost in the late Holocene show signs of significant recent morphodynamic activity. Likely explanations for early-to mid-Holocene stabilisation include (1) substantial changes of soil moisture conditions and related thermodynamics within the active layer affecting frost action, (2) loss of fine-grained substrate matrix from the coarse stripes and hence reduced frost susceptibility, and (3) exhaustion of supply of boulders from the fines-dominated areas. Whereas the sorted stripe data set as a whole did not reproduce the altitudinal gradient characteristic of sorted circles on Juvflye, the strength of the relationship between sorted stripe mean RRock-values and altitude increased with declining slope gradient. Although interpretation of SHD-ages for patterned ground remains challenging, this successful application of the electronic Schmidt-hammer, with its increased efficiency and technical improvements over the mechanical Schmidt-hammer, offers considerable potential for future SHD-studies in both morphodynamic and palaeoclimatic contexts.

show abstract

Section: Schmidt-hammer Exposure-age Dating (Shd)supporting

confidence: 70%

Schmidt-hammer exposure-age dating (SHD) of sorted stripes on Juvflye, Jotunheimen (central South Norway): Morphodynamic and palaeoclimatic implications

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The data for the herbaceous plant communities were acquired in the Mueller glacier foreland, located in Mount Cook National Park (New Zealand). The Mueller glacier foreland has a size of approximately 450 ha and is shaped by a sequence of lateral and latero-frontal moraines formed between 125 y BP and 3,370 ± 290 y BP 54 . Previous studies on vegetation succession in the adjacent East Hooker Valley found distinct plant communities on differently aged terrain with pioneer and early successional stages characterized by the herb Epilobium melanocaulon and the moss Racomitrium lanuginosum , intermediate successional stages with Festuca and Chionochloa grassland and later successional shrubland with woody Dracophyllum spp .…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Kattenborn

Eichel

Fassnacht

2019

Sci Rep

191

110

View full text Add to dashboard Cite

Recent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

show abstract

“…Case study on herb and shrub communities in a New Zealand glacier foreland ('Vegetation Succession') We tested the proposed CNN approach for mapping a spatially complex vegetation succession in the Mueller glacier foreland. Located in Mount Cook National Park (New Zealand), it comprises about 450 ha and is characterized by a sequence of lateral and latero-frontal moraines formed 125-3370 AE 290 years ago (Winkler and Lambiel 2018). Previous studies on vegetation succession in the adjacent East Hooker Valley found distinct plant communities on different aged terrain with pioneer and early successional stages characterized by the herb Epilobium melanocaulon and the moss Racomitrium lanuginosum, intermediate successional stages with Festuca and Chionochloa grassland and later successional shrubland with woody Dracophyllum spp.…”

Section: Study Sites Description and Data Acquisitionmentioning

confidence: 99%

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Kattenborn

Eichel

Wiser

et al. 2020

Remote Sens Ecol Conserv

105

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel-or texturebased mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV-orthoimagery (here 2-5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV-based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2-5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications.

show abstract

Age constraints of rock glaciers in the Southern Alps/New Zealand – Exploring their palaeoclimatic potential

Cited by 39 publications

References 64 publications

Schmidt-hammer exposure-age dating (SHD) of sorted stripes on Juvflye, Jotunheimen (central South Norway): Morphodynamic and palaeoclimatic implications

Schmidt-hammer exposure-age dating (SHD) of sorted stripes on Juvflye, Jotunheimen (central South Norway): Morphodynamic and palaeoclimatic implications

Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

Contact Info

Product

Resources

About