Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry
Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km<sup>2</sup> snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/4/828 |
| _version_ | 1827588461704511488 |
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| author | Sangku Lee Jeongha Park Eunsoo Choi Dongkyun Kim |
| author_facet | Sangku Lee Jeongha Park Eunsoo Choi Dongkyun Kim |
| author_sort | Sangku Lee |
| collection | DOAJ |
| description | Factors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km<sup>2</sup> snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible. |
| first_indexed | 2024-03-09T00:35:47Z |
| format | Article |
| id | doaj.art-b9620a0bd8cc4b2b8b63ea3b9d754ffc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:35:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b9620a0bd8cc4b2b8b63ea3b9d754ffc2023-12-11T18:11:10ZengMDPI AGRemote Sensing2072-42922021-02-0113482810.3390/rs13040828Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based PhotogrammetrySangku Lee0Jeongha Park1Eunsoo Choi2Dongkyun Kim3Department of Civil and Environmental Engineering, Hongik University, Seoul 04066, KoreaDepartment of Civil and Environmental Engineering, Hongik University, Seoul 04066, KoreaDepartment of Civil and Environmental Engineering, Hongik University, Seoul 04066, KoreaDepartment of Civil and Environmental Engineering, Hongik University, Seoul 04066, KoreaFactors influencing the accuracy of UAV-photogrammetry-based snow depth distribution maps were investigated. First, UAV-based surveys were performed on the 0.04 km<sup>2</sup> snow-covered study site in South Korea for 37 times over the period of 13 days under 16 prescribed conditions composed of various photographing times, flight altitudes, and photograph overlap ratios. Then, multi-temporal Digital Surface Models (DSMs) of the study area covered with shallow snow were obtained using digital photogrammetric techniques. Next, the multi-temporal snow depth distribution maps were created by subtracting the snow-free DSM from the multi-temporal DSMs of the study area. Then, snow depth in these UAV-Photogrammetry-based snow maps were compared to the in situ measurements at 21 locations. The accuracy of each of the multi-temporal snow maps were quantified in terms of bias (median of residuals, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>) and precision (the Normalized Median Absolute Deviation, NMAD). Lastly, various factors influencing these performance metrics were investigated. The results are as follows: (1) the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> and NMAD of the eight surveys performed at the optimal condition (50 m flight altitude and 80% overlap ratio) ranged from −2.30 cm to 5.90 cm and from 1.78 cm to 4.89 cm, respectively. The best survey case had −2.30 cm of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula> and 1.78 cm of NMAD; (2) Lower UAV flight altitude and greater photograph overlap lower the NMAD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>; (3) Greater number of Ground Control Points (GCPs) lowers the NMAD and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Q</mi><mrow><mi mathvariant="sans-serif">Δ</mi><mi>D</mi></mrow></msub></mrow></semantics></math></inline-formula>; (4) Spatial configuration and accuracy of GCP coordinates influenced the accuracy of the snow depth distribution map; (5) Greater number of tie-points leads to higher accuracy; (6) Smooth fresh snow cover did not provide many tie-points, either resulting in a significant error or making the entire photogrammetry process impossible.https://www.mdpi.com/2072-4292/13/4/828snowphotogrammetryUAVground control pointsdronemulti-temporal |
| spellingShingle | Sangku Lee Jeongha Park Eunsoo Choi Dongkyun Kim Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry Remote Sensing snow photogrammetry UAV ground control points drone multi-temporal |
| title | Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry |
| title_full | Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry |
| title_fullStr | Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry |
| title_full_unstemmed | Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry |
| title_short | Factors Influencing the Accuracy of Shallow Snow Depth Measured Using UAV-Based Photogrammetry |
| title_sort | factors influencing the accuracy of shallow snow depth measured using uav based photogrammetry |
| topic | snow photogrammetry UAV ground control points drone multi-temporal |
| url | https://www.mdpi.com/2072-4292/13/4/828 |
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