## 6 Conclusions  
This paper presents a method for measuring the length and slope of the dry beach using the 3D point cloud model reconstructed based on UAV imagery. For measuring  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GOR** & **SOR** & **ROR** & **Proposed Method** \\ \hline RMSE & 0.1731 & 0.3458 & 0.1616 & 0.0828 \\ ODR & 1.0009 & 0.4748 & 0.4573 & 0.8967 \\ Recall & 0.7258 & 0.5570 & 0.5478 & 0.7035 \\ Accuracy & 0.7263 & 0.3449 & 0.3323 & 0.6508 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation criteria of denoising method.  
the dry beach length, a region growth algorithm based on the automatic selection of seed points using superpixels was proposed to segment the image. The boundary was identified using an edge extraction operator and converted into three-dimensional spatial coordinates to measure the dry beach length. In the measurement of the dry beach slope, a sliding window filter denoising method for the dry beach point cloud profile was proposed. The results were compared with the field measurement results. The maximum error of dry beach length is 2.25%, and the maximum error of dry beach slope is 2.67%. This shows that the dry beach parameters based on UAV 3D model reconstruction have a high consistency with the field measurement data, with errors remaining within an acceptable range.  
Compared with the traditional method and the method based on image processing, the point cloud model can cover the whole dry beach area and support the measurement of the length and slope of the dry beach at any point. The point cloud data provide abundant three-dimensional spatial information, which can intuitively describe the surface morphology and relief characteristics of the dry beach. At the same time, it can meet the monitoring requirements of relevant regulations. However, because 3D reconstruction takes a long time, it is difficult to achieve real-time monitoring. It is considered that a combination of the dry-beach-monitoring method based on image processing and the method proposed in this study can realize real-time monitoring and provide more spatial information.  
Conceptualization, Y.L.; methodology, Y.L.; software, Y.L. and B.C.; validation, Y.L., B.C. and X.Z.; formal analysis, X.Z.; investigation, X.Z.; resources, G.L.; data curation, Z.Y.; writing--original draft preparation, Y.L.; writing--review and editing, B.C. and Z.Y.; visualization, G.L.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [No. 41974148]; the Natural Resources Science and Technology Project of Hunan Province [Grant No. 2022-01]; and the Science and Technology Research and Development Project of China Railway Co., Ltd. [No. 2022-Special-07].  
Not applicable.  
The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.  
Thanks to the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration for their invaluable support to this research. The authors' special appreciation goes to Editors and anonymous reviewers for valuable comments.  
The authors declare that this study received funding from Science and Technology Research and Development Project of China Railway Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.