Jin Junling, Verbeurgt Jeffrey, De Sloover Lars, Stal Cornelis, Deruyter Greet, Montreuil Anne-Lise, Vos Sander, De Maeyer Philippe, De Wulf Alain
Department of Geography, Ghent University, Krijgslaan 281 S8, 9000 Ghent, Belgium.
Department of Real-estate and Applied Geomatics, University College Ghent, Valentin Vaerwyckweg 1, 9000, Ghent, Belgium.
Int J Appl Earth Obs Geoinf. 2021 Oct;102:102458. doi: 10.1016/j.jag.2021.102458.
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 R 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%).
海滩表面湿度（BSM）是陆地-大气水和能量通量、地下水资源预算以及海岸海滩/沙丘发育的海岸调查中的一个关键属性。在本研究中，首次尝试基于支持向量回归（SVR）从地面激光雷达强度数据估算BSM。采用远程静态地面激光雷达（Riegl VZ-2000）在比利时奥斯坦德-马里亚克克海滩收集高时空分辨率的点云数据。基于现场湿度样本，以反向散射强度、扫描范围和入射角为输入特征，开发了SVR模型来反演BSM。基于模拟的BSM强度样本，充分研究了训练样本的大小和密度对SVR模型预测精度和泛化能力的影响。此外，我们将用于BSM估算的SVR模型的性能与传统的逐步回归（SR）方法和人工神经网络（ANN）进行了比较。结果表明，SVR能够从反向散射强度中准确反演BSM，具有很高的可重复性（平均测试均方根误差为0.71%±0.02%，相关系数为0.98%±0.002%）。径向基函数（RBF）是本研究中SVR模型开发最合适的核函数。在通过SVR模型估算BSM的过程中，扫描几何对强度的影响也能得到准确校正。然而，与SR方法相比，SVR模型的预测精度和泛化性能显著依赖于训练样本的覆盖范围、大小和分布，这表明需要具有均匀分布和代表性的训练样本。SVR模型开发所需的训练样本最小大小为54。在此条件下，SVR的表现与ANN相似，测试均方根误差为1.06%，但即使使用极少的训练样本（仅16个均匀分布的现场样本），SVR的表现仍然可以接受（均方根误差为1.83%），远优于ANN（均方根误差为4.02%）。
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