Search Results (7)

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a single method has inherent limitations. Passive remote sensing is challenged by complex beach illumination and sediment grain size variability. Active remote sensing represented by LiDAR (light detection and ranging) exhibits high sensitivity to moisture, but requires cumbersome intensity correction and may leave data holes in high-moisture areas. Using machine learning, this research proposes a BSM inversion method that fuses UAV (unmanned aerial vehicle) orthophoto brightness with intensity recorded by TLSs (terrestrial laser scanners). First, a back propagation (BP) network rapidly corrects original intensity with in situ scanning data. Second, beach sand grain size is estimated based on the characteristics of the grain size distribution. Then, by applying nearest point matching, intensity and brightness data are fused at the point cloud level. Finally, a new BP network coupled with the fusion data and grain size information enables automatic brightness correction and BSM inversion. A field experiment at Baicheng Beach in Xiamen, China, confirms that this multi-source data fusion strategy effectively integrates key features from diverse sources, enhancing the BP network predictive performance. This method demonstrates robust predictive accuracy in complex beach environments, with an RMSE of 2.63% across 40 samples, efficiently producing high-resolution BSM maps that offer values in studying aeolian sand transport mechanisms. Full article

Geologic remote sensing studies often targets surface cover that is supposed to be invariant or only changing on a geological timescale. In terms of surface material characteristics, this holds for rocks and minerals, but only to a lesser degree for soils (including alluvium, colluvium, regolith or weathered outcrop) and not for vegetation cover, for example. A view unobstructed by clouds, vegetation or fire scars is essential for a persistent observation of surface mineralogy. Sensors with a continuous multi-temporal operation (e.g., Landsat 8 OLI and Sentinel-2 MSI) can provide the data volume needed to come to an optimal seasonal acquisition and the application of data fusion approaches to create an unobstructed view. However, the acquisition environment always changes over time, driven by seasonal changes, illumination changes and the weather. Consequently, the creation of an unobstructed view does not necessarily lead to a repeatable measurement. In this paper, we evaluate the influence of weather and resulting soil moisture conditions over a 3-year period, with alternating dry and wet periods, on the variance of several “geological” spectral indices in a semi-arid area. Sentinel-2 MSI data are chosen to calculate band ratios for green vegetation, ferric and ferrous iron oxide mineralogy and hydroxyl bearing alteration (clay) mineralogy. The data were used “as provided”, meaning that the performance of the atmospheric correction and geometric accuracy is not changed. The results are shown as time-series for selected areas that include solid rock, beach sand, bare soil and natural vegetation surfaces. Results show that spectral index values vary not only between dry and wet periods, but also within dry periods longer than 45 days, as a result of changing soil moisture conditions long after a last rain event has passed. In terms of repeatability of measurements, an overall low soil-moisture level is more important for long-term stability of spectral index values than the occurrence of minor rain events. In terms of creating an unobstructed view, we found that thresholds for NDVI should not be higher than 0.1 when masking vegetation in geological remote sensing, which is lower than what usually is indicated in literature. In conclusion, multi-temporal data are not only important to study dynamic Earth processes, but also to improve mapping of surfaces that are seemingly invariant. As this work is based on a few selected pixels, the obtained results should be considered only indicative and not as a numerical truth. We conclude that multi-temporal data can be used to create an unobstructed view, but also to select the data that give the most repeatability of measurements. Images selection should not be based on a certain number of days without rain in the days preceding data acquisition but aim for the lowest soil moisture conditions. Consequently, weather data should be incorporated to come to an optimal selection of remote sensing imagery, and also when analyzing multi-temporal data. Full article

Anthropogenic activities and climate change in coastal areas require continuous monitoring for a better understanding of environmental evolution and for the implementation of protection strategies. Surface moisture is one of the important drivers of coastal variability because it highly affects shoreward sand transport via aeolian processes. Several methods have been explored for measuring surface moisture at different spatiotemporal resolutions, and in recent years, light detection and ranging (LiDAR) technology has been investigated as a remote sensing tool for high-spatiotemporal-resolution moisture detection. The aim of the present study is the assessment of the performance of a permanent terrestrial laser scanner (TLS) with an original setting located on a high position and hourly scanning of a wide beach area stretching from a swash zone to the base of a dune in order to evaluate the soil moisture at a high spatiotemporal resolution. The reflectance of a Riegl-VZ2000 located in Noordwijk on the Dutch coast was used to assess a new calibration curve that allows the estimation of soil moisture. Three days of surveys were conducted to collect ground-truth soil moisture measurements with a time-domain reflectometry (TDR) sensor at 4 cm depth. Each in situ measurement was matched with the closest reflectance measurement provided by the TLS; the data were interpolated using a non-linear least squares method. A calibration curve that allowed the estimation of the soil moisture in the range of 0–30% was assessed; it presented a root-mean-square error (RMSE) of 4.3% and a coefficient of determination (R-square) of 0.86. As an innovative aspect, the calibration curve was tested under different circumstances, including weather conditions and tidal levels. Moreover, the TDR data collected during an independent survey were used to validate the assessed curve. The results show that the permanent TLS is a highly suitable technique for accurately evaluating the surface moisture variations on a wide sandy beach area with a high spatiotemporal resolution. Full article

The sand surface is considered a critical injury and performance contributing factor in different sports, from beach volleyball to greyhound racing. However, there is still a significant gap in understanding the dynamic behaviour of sport sand surfaces, particularly their vibration behaviour under impact loads. The purpose of this research was to introduce different measurement techniques to the study of sports sand surface dynamic behaviour. This study utilised an experimental drop test, accelerometry, in-situ moisture content and firmness data, to investigate the possible correlation between the sand surface and injuries. The analysis is underpinned by data gathered from greyhound racing and discussed where relevant. Full article

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 × 200 m per minute). Full article

Understanding the spatiotemporal variability of surface moisture on a beach is a necessity to develop a quantitatively accurate predictive model for aeolian sand transport from the beach into the foredune. Here, we analyze laser-derived surface moisture maps with a 1 × 1 m spatial and a 15-min temporal resolution and concurrent groundwater measurements collected during falling and rising tide at the barred Egmond beach, the Netherlands. Consistent with earlier studies, the maps show that the beach can be conceptualized into three surface moisture zones. First, the wet zone just above the low tide level: 18–25%; second, the intertidal zone: 5–25% with large fluctuations. In this zone, surface moisture can decrease with a rate varying between ∼2.5–4% per hour, and cumulatively with 16% during a single falling tide; and, third, the back beach zone: 3–7% (dry). The bar–trough system perturbs this overall zonation, with the moisture characteristics on the bar similar to the upper intertidal beach and the trough always remaining wet. Surface moisture fluctuations are strongly linked to the behavior of groundwater depth and can be described by a ’Van Genuchten-type’ retention curve without hysteresis effects. Applying the Van Genuchten relationship with measured groundwater data allows us to predict surface moisture maps. Results show that the predictions capture the overall surface moisture pattern reasonably well; however, alongshore variability in groundwater level should be improved to refine the predicted surface moisture maps, especially near the sandbar. Full article

In this paper, we explore an approach for annual-scale transport prediction from the intertidal beach, in which we aggregate the surface conditions of the intertidal beach, in particular moisture content and roughness, and use hourly monitoring data of wind speed and wind direction. For our case study area (Egmond Beach, The Netherlands), we include Argus video imagery in our analysis to assess the occurrence of aeolian sand transport. With the approach described to determine a characteristic moisture content value for aeolian transport, we obtained surface moisture values of 1.2% to 3.2% for wind average and wind gust respectively, implying that we need a quite dry beach. This indicates that the main area for aeolian transport corresponds to the upper part of the intertidal source, most likely the region between mean high tide line and spring high tide line. Full article