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[Objective] To compare the applicability of terrain change monitoring algorithms in the pisha sandstone areas of the loess plateau. [Methods] A branch gully Telagouagou Huangfuchuan was taken as the research object, including digital elevation model of difference (DoD), cloud to cloud (C2C), cloud to mesh (C2M), and multiscale model to model cloud comparison (M3C2). Point cloud data employed to operate the four algorithms were produced using the SfM technique based on images acquired by UAV between July 2022 and March 2023. The impact of point density changes in the accuracy of the employed algorithms was also investigated. [Results] (1) All four algorithms were capable of effectively monitoring large surface changes. Among them, the M3C2 algorithm performed the best with the highest accuracy (R2=0.953, p

Unmanned aerial vehicle (UAV)-based nap-of-the-object photogrammetry techniques can be utilized to periodically monitor the erosion of nearly vertical cliffs. However, the broader applicability of such techniques is hindered by the necessity of deploying multiple ground control points around collapsing walls. This study aims to accurately assess Benggang erosion before and after the rainy season by analyzing the optimal flight proximity distance using close-range photogrammetric techniques. The assessment centers on positioning accuracy, point cloud data, and digital surface model (DSM) data. Nap-of-the-object photogrammetry techniques are integrated with control-free image methods to conduct aerial surveys of Benggang, generating high-resolution three-dimensional (3D) DSMs. The feasibility of this control-free-image-based nap-of-the-object photogrammetry technique is evaluated based on positioning accuracy and measurement errors, comparing the generated DSMs with real-time kinematic (RTK) measured coordinate data. The results indicate that a flight proximity distance of 20 m is optimal for obtaining data in the Benggang area using control-free-image-based nap-of-the-object photogrammetry. This scheme yields an average reprojection error of approximately 0.01 pixels in data processing before and after rainfall, showing strong consistency in the spatial distribution of the two-stage 3D models. The mean absolute error in planar accuracy is between 0.01 m and 0.02 m, and that in elevation accuracy is approximately 0.03 m, with the lowest errors reaching the millimeter level. Therefore, control-free images combined with nap-of-the-object photogrammetry techniques can meet relevant demands for monitoring landslide erosional areas, providing technical support for extensive, safe, and efficient Benggang erosion monitoring.

The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)–least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.

SAET (Shoreline Analysis and Extraction Tool) is a novel open-source tool to enable the completely automatic detection of shoreline position changes using the optical imagery acquired by the Sentinel-2 and Landsat 8 and 9 satellites. SAET has been developed within the ECFAS (European Coastal Flood Awareness System) project, which is intended to be the first European service for coastal flood forecasting, management, and recovery analysis. The tool is developed to characterise the shoreline response associated with punctual events such as coastal storms as well as any other phenomenon. For a given beach segment, SAET facilitates the selection of the satellite images closest in time to the analysed events that offer an adequate cloud coverage level for analysing the shoreline change. Subsequently, the tool automatically downloads the images from their official repositories, pre-processes them and extracts the shoreline position with sub-pixel accuracy. In order to do so, an initial approximate definition of the shoreline is carried out at the pixel level using a water index thresholding, followed by an accurate extraction operating on the shortwave infrared bands to produce a sub-pixel line in vector formats (points and lines). The tool offers different settings to be adapted to the different coastal environments and beach typologies. Its main advantages refer to its autonomy, its efficiency in extracting complete satellite scenes, its flexibility in adapting to different environments and conditions, and its high subpixel accuracy. This work presents an accuracy assessment on a long Mediterranean sandy beach of SDSs extracted from L8 and S2 imagery against coincident alongshore reference lines, showing an accuracy of about 3 m RMSE. At the same time, the work shows an example of the usage of SAET for characterising the response to Storm Gloria (January 2020) on the beaches of Valencia (E Spain). SAET provides an efficient and completely automatic workflow that leads to accurate SDSs while only relying on publicly available information. The tool appears to be a useful extraction tool for beach monitoring, both for public administrations and individual users.

ABSTRACT The method used to sample the runoff collected from soil erosion plots can be a significant source of error. In this study, we performed a laboratory experiment to evaluate the efficiency of the runoff sampling method most commonly used in Brazil. It is based on the manual homogenization and sampling of the collected runoff. Using soil material with 583 g kg–1 of sand and 89 g kg–1 of clay, the manual sampling method was tested for its ability to produce representative samples of artificial suspensions with a concentration of 2, 10 and 50 g L–1 of total solids. An underestimation of 30 % or more of the concentration of total solids was observed, with a variation of the same magnitude (CV between 20 and 45 %). We then developed a prototype sample splitter to replace the manual sampling method and tested it using the same artificial suspensions. The splitter was efficient in producing samples representative of the artificial suspensions, even without altering the particle size distribution of the total solids. Both absolute percentage errors (|< 5 %|) and the variation between five replicates (CV < 3 %) were small. The problems with the manual method are due to the inefficient homogenization that facilitates the differential sedimentation of particles of different sizes. If these problems are also found in other areas, then the prototype that we developed is a reasonable alternative.