The measurement of surface moisture on beaches is vital for studying aeolian sand transport mechanisms, but existing techniques are not adequate for monitoring the surface moisture dynamics over a substantial beach section. In this study, we investigated the suitability of a new remote sensing method to monitor the spatiotemporal variation in surface moisture on a sandy beach using a long-range static terrestrial laser scanner (TLS). The TLS was permanently deployed on top of a 42 m high building overlooking the study site at Ostend-Mariakerke, Belgium. Considering the effect of target surface roughness on the intensity and the laboratory’s length limitation, a new intensity correction method is proposed which only uses the field point cloud data measured on a homogenous beach surface (without time-consuming indoor experiments). Based on the corrected intensity data, the relation of the beach surface moisture to the corrected intensity was modeled by an exponential model with a correlation-coefficient squared of 0.92. A moisture estimation model was developed which can directly derive the beach surface moisture from the original intensity data of the TLS with a standard error of 2.27%. The hourly surface moisture dynamics across two tidal cycles on the beach were investigated as a case study, in which the point clouds derived from corresponding unmanned aerial vehicle (UAV) imagery are utilized to improve the calculation accuracy of the incidence angles of TLS point clouds at long distances. Results reveal that, after the intensity correction, the long-range static TLS is an extremely suitable technique to monitor the surface moisture dynamics (daytime and nighttime) over a substantial beach section (hundreds of meters) at a high scanning frequency (minutes to hours).@en

Beach Surface Moisture (BSM) is a key attribute in the coastal investigations of land-atmospheric water and energy fluxes, groundwater resource budgets and coastal beach/dune development. In this study, an attempt has been made for the first time to estimate BSM from terrestrial LiDAR intensity data based on the Support Vector Regression (SVR). A long-range static terrestrial LiDAR (Riegl VZ-2000) was adopted to collect point cloud data of high spatiotemporal resolution on the Ostend-Mariakerke beach, Belgium. Based on the field moisture samples, SVR models were developed to retrieve BSM, using the backscattered intensity, scanning ranges and incidence angles as input features. The impacts of the training samples’ size and density on the predictive accuracy and generalization capability of the SVR models were fully investigated based on simulated BSM-intensity samples. Additionally, we compared the performance of the SVR models for BSM estimation with the traditional Stepwise Regression (SR) method and the Artificial Neural Network (ANN). Results show that SVR could accurately retrieve the BSM from the backscattered intensity with high reproducibility (average test RMSE of 0.71% ± 0.02% and R² of 0.98% ± 0.002%). The Radial Basis Function (RBF) was the most suitable kernel for SVR model development in this study. The impacts of scanning geometry on the intensity could also be accurately corrected in the process of estimating BSM by the SVR models. However, compared to the SR method, the predictive accuracy and generalization performance of SVR models were significantly dependent on the training samples’ coverage, size and distribution, suggesting the need for the training samples of uniform distribution and representativeness. The minimum size of training samples required for SVR model development was 54. Under this condition, SVR performed similarly to ANN with a test RMSE of 1.06%, but SVR still performed acceptably (with an RMSE of 1.83%) even using extremely few training samples (only 16 field samples of uniform distribution), far better than the ANN (with an RMSE of 4.02%).

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