A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR
Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2022.2160842 |
| _version_ | 1797678510791196672 |
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| author | Zohreh Alijani Julien Meloche Alexander McLaren John Lindsay Alexandre Roy Aaron Berg |
| author_facet | Zohreh Alijani Julien Meloche Alexander McLaren John Lindsay Alexandre Roy Aaron Berg |
| author_sort | Zohreh Alijani |
| collection | DOAJ |
| description | Surface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R2 = 0.70) and a relatively close agreement with pin profiler (R2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas. |
| first_indexed | 2024-03-11T23:00:49Z |
| format | Article |
| id | doaj.art-bac5e245e6894c75992be099a9d66583 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:49Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-bac5e245e6894c75992be099a9d665832023-09-21T14:57:12ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552022-12-011512422243910.1080/17538947.2022.21608422160842A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDARZohreh Alijani0Julien Meloche1Alexander McLaren2John Lindsay3Alexandre Roy4Aaron Berg5University of GuelphUniversité de SherbrookeUniversity of GuelphUniversity of GuelphUniversité du Québec à Trois-RivièresUniversity of GuelphSurface roughness plays an important role in microwave remote sensing. In the agricultural domain, surface roughness is crucial for soil moisture retrieval methods that use electromagnetic surface scattering or microwave radiative transfer models. Therefore, improved characterization of Soil Surface Roughness (SSR) is of considerable importance. In this study, three approaches, including a standard pin profiler, a LiDAR point cloud generated from an iPhone 12 Pro, and a Structure from Motion (SfM) photogrammetric point cloud, were applied over 24 surface profiles with different roughness variations to measure surface roughness. The objective of this study was to evaluate the capability of smartphone-based LiDAR technology to measure surface roughness parameters and compare the results of this technique with the more common approaches. Results showed that the iPhone LiDAR technology, when point cloud data is captured in a fine-resolution mode, has a significant correlation with SfM photogrammetry (R2 = 0.70) and a relatively close agreement with pin profiler (R2 = 0.60). However, this accuracy tends to be greater for random surfaces and rough profiles with row structure orientations. The results of this study confirm that smartphone-based LiDAR can be used as a cost-effective, fast, and time-efficient alternative tool for measuring surface roughness, especially for rough, wide, and inaccessible areas.http://dx.doi.org/10.1080/17538947.2022.2160842lidarsurface roughnessstructure from motioniphonepin profiler |
| spellingShingle | Zohreh Alijani Julien Meloche Alexander McLaren John Lindsay Alexandre Roy Aaron Berg A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR International Journal of Digital Earth lidar surface roughness structure from motion iphone pin profiler |
| title | A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR |
| title_full | A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR |
| title_fullStr | A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR |
| title_full_unstemmed | A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR |
| title_short | A comparison of three surface roughness characterization techniques: photogrammetry, pin profiler, and smartphone-based LiDAR |
| title_sort | comparison of three surface roughness characterization techniques photogrammetry pin profiler and smartphone based lidar |
| topic | lidar surface roughness structure from motion iphone pin profiler |
| url | http://dx.doi.org/10.1080/17538947.2022.2160842 |
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