Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy
Unmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deplo...
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/9/2297 |
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| author | Song Shu Ok-Youn Yu Chris Schoonover Hongxing Liu Bo Yang |
| author_facet | Song Shu Ok-Youn Yu Chris Schoonover Hongxing Liu Bo Yang |
| author_sort | Song Shu |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deployment of GCPs on the accuracy of snow depth estimates. For 15 GCPs in our study area, we surveyed each of their coordinates using an ordinary global positioning system (GPS) and a differential GPS, producing two sets of GCP measurements (hereinafter, the low-accuracy and high-accuracy sets). The two sets of GCP measurements were then incorporated into SfM processing of UAV images by following two deployment strategies to create snow-off and snow-on DSMs and then to retrieve snow depth. In Strategy A, the same GCP measurements in each set were used to create both the snow-on and snow-off DSMs. In Strategy B, each set of GCP measurements was divided into two sub-groups, one sub-group for creating snow-on DSMs and the other sub-group for snow-off DSMs. The accuracy of snow depth estimates was evaluated in comparison to concurrent in-situ snow depth measurements. The results showed that Strategy A, using both the low-accuracy and high-accuracy sets, generated accurate snow depth estimates, while in Strategy B, only the high-accuracy set could generate reliable snow depth estimates. The results demonstrated that the deployment of GCPs had a significant influence on UAV-based SfM snow depth retrieval. When accurate GCP measurements cannot be guaranteed (e.g., in mountainous regions), Strategy A is the optimal option for producing reliable snow depth estimates. When highly accurate GCP measurements are available (e.g., collected by differential GPS in open space), both deployment strategies can produce accurate snow depth estimates. |
| first_indexed | 2024-03-11T04:08:01Z |
| format | Article |
| id | doaj.art-82fee159b3b14b95976bcedb68098a4f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:08:01Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-82fee159b3b14b95976bcedb68098a4f2023-11-17T23:38:13ZengMDPI AGRemote Sensing2072-42922023-04-01159229710.3390/rs15092297Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment StrategySong Shu0Ok-Youn Yu1Chris Schoonover2Hongxing Liu3Bo Yang4Department of Geography and Planning, Appalachian State University, Boone, NC 28608, USADepartment of Sustainable Technology and the Built Environment, Appalachian State University, Boone, NC 28608, USADepartment of Sustainable Technology and the Built Environment, Appalachian State University, Boone, NC 28608, USADepartment of Geography, The University of Alabama, Tuscaloosa, AL 35487, USADepartment of Urban and Regional Planning, San Jose State University, San Jose, CA 95192, USAUnmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deployment of GCPs on the accuracy of snow depth estimates. For 15 GCPs in our study area, we surveyed each of their coordinates using an ordinary global positioning system (GPS) and a differential GPS, producing two sets of GCP measurements (hereinafter, the low-accuracy and high-accuracy sets). The two sets of GCP measurements were then incorporated into SfM processing of UAV images by following two deployment strategies to create snow-off and snow-on DSMs and then to retrieve snow depth. In Strategy A, the same GCP measurements in each set were used to create both the snow-on and snow-off DSMs. In Strategy B, each set of GCP measurements was divided into two sub-groups, one sub-group for creating snow-on DSMs and the other sub-group for snow-off DSMs. The accuracy of snow depth estimates was evaluated in comparison to concurrent in-situ snow depth measurements. The results showed that Strategy A, using both the low-accuracy and high-accuracy sets, generated accurate snow depth estimates, while in Strategy B, only the high-accuracy set could generate reliable snow depth estimates. The results demonstrated that the deployment of GCPs had a significant influence on UAV-based SfM snow depth retrieval. When accurate GCP measurements cannot be guaranteed (e.g., in mountainous regions), Strategy A is the optimal option for producing reliable snow depth estimates. When highly accurate GCP measurements are available (e.g., collected by differential GPS in open space), both deployment strategies can produce accurate snow depth estimates.https://www.mdpi.com/2072-4292/15/9/2297UAVstructure from motion (SfM)snow depthground control points (GCP) |
| spellingShingle | Song Shu Ok-Youn Yu Chris Schoonover Hongxing Liu Bo Yang Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy Remote Sensing UAV structure from motion (SfM) snow depth ground control points (GCP) |
| title | Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy |
| title_full | Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy |
| title_fullStr | Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy |
| title_full_unstemmed | Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy |
| title_short | Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy |
| title_sort | unmanned aerial vehicle based structure from motion technique for precise snow depth retrieval implication for optimal ground control point deployment strategy |
| topic | UAV structure from motion (SfM) snow depth ground control points (GCP) |
| url | https://www.mdpi.com/2072-4292/15/9/2297 |
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