Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively.

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.

In this work we highlight the importance of radiative convection as a mixing mechanism in a large, ice‐free lake (Lake Michigan, USA), where solar heating of waters below the temperature of maximum density drives vertical convection during the vernal turnover. Measurements taken over a 2‐week period at a 55‐m deep site demonstrate the ability of radiative convection to mix the entire water column. Observations show a diurnal cycle in which solar heating drives a steady deepening of the convective mixed layer throughout the day (dH_CML/dt = 12.8 m/hr), followed by surface‐cooling‐induced restratification during the night. Radiative convection is linked to a dramatic enhancement in turbulence characteristics, including both turbulent kinetic energy dissipation (ϵ: 10⁻⁹–10⁻⁷ W/kg) and turbulent scalar diffusivity (K_z: 10⁻³–10⁻¹ m²/s), suggesting that radiative convection plays a major role in driving vertical mixing throughout the water column during the isothermal spring.

Turbulent mixing controls the vertical transfer of heat, gases and nutrients in stratified water bodies, shaping their response to environmental forcing. Nevertheless, due to technical limitations, the redistribution of wind-derived energy fuelling turbulence within stratified lakes has only been mapped over short (sub-annual) timescales. Here we present a year-round observational record of energy fluxes in the large Lake Geneva. Contrary to the standing view, we show that the benthic layers are the main locus for turbulent mixing only during winter. Instead, most turbulent mixing occurs in the water-column interior during the stratified summer season, when the co-occurrence of thermal stability and lighter winds weakens near-sediment currents. Since stratified conditions are becoming more prevalent –possibly reducing turbulent fluxes in deep benthic environments–, these results contribute to the ongoing efforts to anticipate the effects of climate change on freshwater quality and ecosystem services in large lakes.