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Constant changes occur in coastal areas over different timescales, requiring observation and modeling. Specifically, modeling morphological changes resulting from short-term events, such as storms, is of great importance in coastal management. Parameter calibration is necessary to achieve more accurate simulations of process-based models that focus on specific locations and event characteristics. In this study, the XBeach depth-averaged model was adopted to simulate subaerial data pre- and post-storms, and overwash phenomena were observed using the data acquired through unmanned aerial vehicles. The parameters used for the model calibration included those proposed in previous studies. However, an emphasis was placed on calibrating the parameters related to sediment transport that were directly associated with overwash and deposition. Specifically, the parameters corresponding to the waveform parameters, wave skewness, and wave asymmetry were either integrated or separated to enable an adequate representation of the deposition resulting from overwash events. The performance and sensitivity of the model to changes in volume were assessed. Overall, the waveform parameters exhibit significant sensitivity to volume changes, forming the basis for calibrating the deposition effects caused by overwashing. These results are expected to assist in the more effective selection and calibration of parameters for simulating sediment deposition due to overwash events. [ABSTRACT FROM AUTHOR]

Although sandy islands in arid environments are vital protection sites for endemic species, they face constant natural and anthropogenic hazards, such as storm surge impacts and the occasional presence of off-road vehicles. Monitoring the sedimentary dune-beach displacement and balance is essential because sediment transportation usually does not depend on external sources, such as rivers. The latest generation of geomatic applications may be relevant to understanding coastal vulnerability due to their ability to acquire and process spatial data at unprecedented scales. The objective of this study was to analyze the sedimentary dynamics of a distinctive dune corridor on Altamura Island in the Gulf of California, Mexico. We compared three ultra-high spatial resolution digital surface models (DSMs) with geomorphic change detection (DoD), covering the 1150 m coastal stretch. We used light detection and ranging (LiDAR) data and analyzed changes in the coastline with the semi-automatic CoastSat tool. Although the orthomosaics and the DSM collected in April, June, and October 2021 show different conditions in sediment distribution along the beach-dune interface, depending on the direction and intensity of the wind, the two DoDs showed a constant sediment distribution balance of ~13 M m3 between April and June and June and October. LiDAR data along the 40-km length of the sandy island indicate that the entire island could present a similar sedimentation pattern between the dune and beach interface. The CoastSat data indicate a constant accretion of 125 m in the beach-ocean interface between 2015 and 2022. This study demonstrates that the sediment balance between the dune and the beach on arid sandy islands is vital for conserving their shoreline and all associated coastal ecosystems. [ABSTRACT FROM AUTHOR]

Following consecutive years of governance efforts, there has been a substantial reduction in sediment transport in the Yellow River, resulting in significant changes in its water–sediment dynamics. This necessitates precise monitoring of sediment-bearing tributary inflows, a crucial requirement for effective governance strategies on the Loess Plateau's current developmental stage. While satellite remote sensing technology has been widely used to estimate suspended particulate matter concentration (CSPM) in open water bodies like oceans and lakes, its application in narrow rivers presents challenges related to hybrid pixel and proximity effects. As a result, the effectiveness and competence of satellite remote sensing in monitoring CSPM in such confined river environments are reduced. This study attempted to use unmanned aerial vehicle (UAV) remote sensing with multispectral technology to invert CSPM in the Wuding River, a sediment-bearing Yellow River tributary. A novel CSPM concentration inversion model was introduced for highly turbid river settings. The results showed that the accuracy of the new band ratio model in this study is significantly improved compared with the existing models. The validation dataset had a coefficient of determination (R2) of 0.83, a root mean square error (RMSE) of 3.73 g/L, and a mean absolute percentage error (MAPE) of 44.95% (MAPE is 40.68% at 1–20 g/L, and 12.37% at >20 g/L). On this basis, the UAV also monitored the impacts of heavy rainfall on the CSPM, resulting in a rapid rise and fall in CSPM over a period of ten hours. This study demonstrated the potential of UAV remote sensing for CSPM monitoring in extremely turbid narrow rivers (tens to tens of meters), especially before and after rainfall sediment production events, which can provide technical support for accurate sediment management and source identification in the main tributaries of the Yellow River and help realize the goal of high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Continuous monitoring of the varying topographical characteristics of shorelines is important for effective coastal management. Closed-circuit television (CCTV) cameras are installed to accumulate photographic data on coastal topographical changes. The overall change in the coastal waters can be intuitively understood from the images. However, the amount of three-dimensional (3D) changes that can be grasped is limited. To address this, studies have employed aerial photogrammetry, which is the use of unmanned aerial vehicles (UAVs) to capture aerial pictures, construct 3D models of target areas, and perform analysis through scale-invariant feature transform and structure from motion technologies. Although highly efficient, this technique requires several ground-control points (GCPs), which could corrupt the overall imagery. This study designs real-time kinematics—global navigation satellite system (RTK–GNSS) UAV, which requires few GCPs. To evaluate the positional accuracy of the captured UAV orthographic images and digital surface models (DSMs) used for precise coastal terrain measurements, a virtual reference service survey was performed to determine the vertical errors. The R-squared was 0.985, which is close to 1.0. Short-term and one-year topographic changes before and after a storm were investigated using time-series UAV image data after a coastal maintenance project. Analysis of the coefficient of variation in the beach volume for one year revealed that submerged breakwater reduced erosion during high wave resistance. The submerged breakwater located in the center exhibited variability similar to the opening. Hence, this method is more suitable for periodically monitoring coastal areas. [ABSTRACT FROM AUTHOR]

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model's limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors' better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams' life expectancy. [ABSTRACT FROM AUTHOR]

River ice segmentation, used for surface ice concentration estimation, is important for validating river processes and ice-formation models, predicting ice jam and flooding risks, and managing water supply and hydroelectric power generation. Furthermore, discriminating between anchor ice and frazil ice is an important factor in understanding sediment transport and release events. Modern deep learning techniques have proved to deliver promising results; however, they can show poor generalization ability and can be inefficient when hardware and computing power is limited. As river ice images are often collected in remote locations by unmanned aerial vehicles with limited computation power, we explore the performance-latency trade-offs for river ice segmentation. We propose a novel convolution block inspired by both depthwise separable convolutions and local binary convolutions giving additional efficiency and parameter savings. Our novel convolution block is used in a shallow architecture which has 99.9% fewer trainable parameters, 99% fewer multiply–add operations, and 69.8% less memory usage than a UNet, while achieving virtually the same segmentation performance. We find that the this network trains fast and is able to achieve high segmentation performance early in training due to an emphasis on both pixel intensity and texture. When compared to very efficient segmentation networks such as LR-ASPP with a MobileNetV3 backbone, we achieve good performance (mIoU of 64) 91% faster during training on a CPU and an overall mIoU that is 7.7% higher. We also find that our network is able to generalize better to new domains such as snowy environments. [ABSTRACT FROM AUTHOR]

Salt marsh evolution is strongly affected by tidal processes and ecology, which regulate sediment accretion and erosional rates. A balance between marsh erosion and deposition in a restored tidal wetland is crucial for analyzing restoration strategies to adopt in a natural context. Here, we present an integrated approach monitoring salt marsh seasonal changes over several months in a microtidal restored salt marsh of the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island (MD, USA). The project is undertaken at a restoration site where sediment dredged from the shipping channels in the upper Chesapeake Bay is being used to restore a tidal marsh habitat in mid-Chesapeake Bay. We flew an Unmanned Aerial Vehicle (UAV) with an RGB and a multispectral camera to obtain a high-resolution map of the planimetric position of vegetation and to monitor the health of the marsh vegetation in diverse seasons. Due to its extension of 400 m by 400 m, a total of four flight plans were necessary to cover the entire marsh flying at a 40 m altitude obtaining a 2 cm Ground Sample Distance (GSD). This technique provides reliable results at a very low cost, enabling an accurate assessment of the marsh platforms to be conducted over time, due to both the very high spatial resolution and the precise georeferencing of the images for the comparisons. Our results show seasonal variability in the two dominant species colonizing the low marsh, Spartina alterniflora, and high marsh, Sporobolus pumilus. While the lower marshes showed a higher variability along seasons, the up-land vegetation showed persistent green foliage during cold seasons. Detecting salt marsh evolution and seasonality coupled with field measurements can help to improve the accuracy of hydrodynamic and sediment transport models. Understanding the drivers of salt marsh evolution is vital for informing restoration practices and designs, in order to improve coastal resilience, and develop and coastal management strategies. [ABSTRACT FROM AUTHOR]

Monitoring the morphological evolution of a river-mouth bar is of both practical and scientific importance. A large amount of sediment is transported from a river to surrounding littoral cells via a deltaic bar after an extreme weather event. However, it is often not feasible to capture drastic morphological changes in the short term with conventional bathymetric surveys. This paper presents a depth-inversion method based on unmanned aerial vehicle technology to estimate two-dimensional bathymetry from video-sensed swell propagation. The estimation algorithm is tested over four cases with varying wave and bathymetric conditions and is validated with transect survey data. The test results suggest that the method can estimate deltaic-bar topography in front of a river mouth with a root-mean-square error of <0.5 m. The applicable range is limited by wave breaking in the inner bar and up to a depth of ~8 m, where swell intensity signals become ambiguous. A comparison of the different cases shows that the method works better under calm weather conditions with dominant swells propagating from non-local sources. Significant morphological changes of a river-mouth bar due to a powerful typhoon are successfully detected by observations right before and after the event. [ABSTRACT FROM AUTHOR]

Coastal environments are usually characterized by a brittle balance, especially in terms of sediment transportation. The formation of dunes, as well as their sudden destruction as a result of violent storms, affects this balance in a significant way. Moreover, the growth of vegetation on the top of the dunes strongly influences the consequent growth of the dunes themselves. This work presents the results obtained through a long-term monitoring of a complex dune system by the use of Unmanned Aerial Vehicles (UAVs). Six different surveys were carried out between November 2015 and December 2017 in the littoral of Rosolina Mare (Italy). Aerial photogrammetric data were acquired during flight repetitions by using a DJI Phantom 3 Professional with the camera in a nadiral arrangement. The processing of the captured images consisted of the reconstruction of a three-dimensional model using the Structure-from-Motion (SfM). Each model was framed in the European Terrestrial Reference System (ETRS) using GNSS geodetic receivers in Network Real Time Kinematic (NRTK). Specific data management was necessary due to the vegetation by filtering the dense cloud. This task was performed by both performing a slope detection and a removal of the residual outliers. The final products of this approach were thus represented by Digital Elevation Models (DEMs) of the sandy coastal section. In addition, DEMs of Difference (DoD) were also computed for the purpose of monitoring over time and detecting variations. The accuracy assessment of the DEMs was carried out by an elevation comparison through especially GNSS-surveyed points. Relevant cross sections were also extracted and compared. The use of the Structure-from-Motion approach by UAVs finally proved to be both reliable and time-saving thanks to quicker in situ operations for the data acquisition and an accurate reconstruction of high-resolution elevation models. The low cost of the system and its flexibility represent additional strengths, making this technique highly competitive with traditional ones. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) systems, sensors, and photogrammetric processing techniques have enabled timely and highly detailed three-dimensional surface reconstructions at a scale that bridges the gap between conventional remote-sensing and field-scale observations. In this work 29 rotary and fixed-wing UAV surveys were conducted during multiple field campaigns, totaling 47 flights and over 14.3 km2, to document permafrost thaw subsidence impacts on or close to road infrastructure in the Northwest Territories, Canada. This paper provides four case studies: (1) terrain models and orthomosaic time series revealed the morphology and daily to annual dynamics of thaw-driven mass wasting phenomenon (retrogressive thaw slumps; RTS). Scar zone cut volume estimates ranged between 3.2 × 103 and 5.9 × 106 m3. The annual net erosion of RTS surveyed ranged between 0.35 × 103 and 0.39 × 106 m3. The largest RTS produced a long debris tongue with an estimated volume of 1.9 × 106 m3. Downslope transport of scar zone and embankment fill materials was visualized using flow vectors, while thermal imaging revealed areas of exposed ground ice and mobile lobes of saturated, thawed materials. (2) Stratigraphic models were developed for RTS headwalls, delineating ground-ice bodies and stratigraphic unconformities. (3) In poorly drained areas along road embankments, UAV surveys detected seasonal terrain uplift and settlement of up to 0.5 m (>1700 m2 in extent) as a result of injection ice development. (4) Time series of terrain models highlighted the thaw-driven evolution of a borrow pit (6.4 × 105 m3 cut volume) constructed in permafrost terrain, whereby fluvial and thaw-driven sediment transfer (1.1 and 3.9 × 103 m3 a−1 respectively) was observed and annual slope profile reconfiguration was monitored to gain management insights concerning site stabilization. Elevation model vertical accuracies were also assessed as part of the case studies and ranged between 0.02 and 0.13 m Root Mean Square Error. Photogrammetric models processed with Post-processed Kinematic image solutions achieved similar accuracies without ground control points over much larger and complex areas than previously reported. The high resolution of UAV surveys, and the capacity to derive quantitative time series provides novel insights into permafrost processes that are otherwise challenging to study. The timely emergence of these tools bridges field-based research and applied studies with broad-scale remote-sensing approaches during a period when climate change is transforming permafrost environments. [ABSTRACT FROM AUTHOR]