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Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Institute for Aerial Survey and Earth Sciences
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805034/ https://www.ncbi.nlm.nih.gov/pubmed/35125982 http://dx.doi.org/10.1016/j.jag.2021.102458 |
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author | Jin, Junling Verbeurgt, Jeffrey De Sloover, Lars Stal, Cornelis Deruyter, Greet Montreuil, Anne-Lise Vos, Sander De Maeyer, Philippe De Wulf, Alain |
author_facet | Jin, Junling Verbeurgt, Jeffrey De Sloover, Lars Stal, Cornelis Deruyter, Greet Montreuil, Anne-Lise Vos, Sander De Maeyer, Philippe De Wulf, Alain |
author_sort | Jin, Junling |
collection | PubMed |
description | 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(2) 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%). |
format | Online Article Text |
id | pubmed-8805034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | International Institute for Aerial Survey and Earth Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-88050342022-02-03 Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data Jin, Junling Verbeurgt, Jeffrey De Sloover, Lars Stal, Cornelis Deruyter, Greet Montreuil, Anne-Lise Vos, Sander De Maeyer, Philippe De Wulf, Alain Int J Appl Earth Obs Geoinf Article 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(2) 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%). International Institute for Aerial Survey and Earth Sciences 2021-10 /pmc/articles/PMC8805034/ /pubmed/35125982 http://dx.doi.org/10.1016/j.jag.2021.102458 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jin, Junling Verbeurgt, Jeffrey De Sloover, Lars Stal, Cornelis Deruyter, Greet Montreuil, Anne-Lise Vos, Sander De Maeyer, Philippe De Wulf, Alain Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data |
title | Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data |
title_full | Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data |
title_fullStr | Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data |
title_full_unstemmed | Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data |
title_short | Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data |
title_sort | support vector regression for high-resolution beach surface moisture estimation from terrestrial lidar intensity data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805034/ https://www.ncbi.nlm.nih.gov/pubmed/35125982 http://dx.doi.org/10.1016/j.jag.2021.102458 |
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