English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Differencing of digital surface models derived from structure from motion(SfM) processing of airborne imagery has been used to produce snow depth (SD) maps withbetween ∼2 and ∼15  cm horizontal resolution and accuracies of ±10  cm over relatively flat surfaces with little or no vegetation andover alpine regions. This study builds on these findings by testing twohypotheses across a broader range of conditions: (i) that the verticalaccuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle(UAV)systems can be adequately modelled using conventional photogrammetric theoryand (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-coveredand snow-free surface. A total of 71 UAV missions were flown over five sites,ranging from short grass to a regenerating forest, with ephemeral snowpacks.Point cloud geolocation performance agreed with photogrammetric theory thatpredicts uncertainty is proportional to UAV altitude and linearly related tohorizontal uncertainty. The root-mean-square difference (RMSD) over theobservation period, in comparison to the average of in situ measurementsalong ∼50  m transects, ranged from 1.58 to 10.56 cm for weekly SD andfrom 2.54 to 8.68 cm for weekly SD change. RMSD was not related tomicrotopography as quantified by the snow-free surface roughness. SD changeuncertainty was unrelated to vegetation cover but was dominated by outlierscorresponding to rapid in situ melt or onset; the median absolute differenceof SD change ranged from 0.65 to 2.71 cm. These results indicate that theaccuracy of UAV-based estimates of weekly snow depth change was, exceptingconditions with deep fresh snow, substantially better than for snow depth andwas comparable to in situ methods.

the cryosphere

Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos