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

Spatio-temporal measurements of landform evolution provide the\udbasis for process-based theory formulation and validation. Over time, field\udmeasurements of landforms have increased significantly worldwide, driven\udprimarily by the availability of new surveying technologies. However, there\udis no standardized or coordinated effort within the scientific\udcommunity to collect morphological data in a dependable and reproducible\udmanner, specifically when performing long-term small-scale process\udinvestigation studies. Measurements of the same site using identical methods\udand equipment, but performed at different time periods, may lead to incorrect\udestimates of landform change as a result of three-dimensional registration\uderrors. This work evaluated measurements of an ephemeral gully channel\udlocated on agricultural land using multiple independent survey techniques for\udlocational accuracy and their applicability in generating information for model\uddevelopment and validation. Terrestrial and unmanned aerial vehicle\udphotogrammetry platforms were compared to terrestrial lidar, defined herein\udas the reference dataset. Given the small scale of the measured landform,\udthe alignment and ensemble equivalence between data sources was addressed through postprocessing. The utilization of ground control points\udwas a\udprerequisite to three-dimensional registration between datasets and improved\udthe\udconfidence in the morphology information generated. None of the methods\udwere without limitation; however, careful attention to project preplanning\udand data nature will ultimately guide the temporal efficacy and practicality of\udmanagement decisions

Quantifying uncertainty in high-resolution remotely sensed topographic surveys for ephemeral gully channel monitoring