Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan
In this study, six different periods of digital terrain model (DTM) data obtained from various flight vehicles by using the techniques of aerial photogrammetry, airborne LiDAR (ALS), and unmanned aerial vehicles (UAV) were adopted to discuss the errors and applications of these techniques. Error est...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-02-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/3/199 |
| _version_ | 1819003251108872192 |
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| author | Yu-Chung Hsieh Yu-Chang Chan Jyr-Ching Hu |
| author_facet | Yu-Chung Hsieh Yu-Chang Chan Jyr-Ching Hu |
| author_sort | Yu-Chung Hsieh |
| collection | DOAJ |
| description | In this study, six different periods of digital terrain model (DTM) data obtained from various flight vehicles by using the techniques of aerial photogrammetry, airborne LiDAR (ALS), and unmanned aerial vehicles (UAV) were adopted to discuss the errors and applications of these techniques. Error estimation provides critical information for DTM data users. This study conducted error estimation from the perspective of general users for mountain/forest areas with poor traffic accessibility using limited information, including error reports obtained from the data generation process and comparison errors of terrain elevations. Our results suggested that the precision of the DTM data generated in this work using different aircrafts and generation techniques is suitable for landslide analysis. Especially in mountainous and densely vegetated areas, data generated by ALS can be used as a benchmark to solve the problem of insufficient control points. Based on DEM differencing of multiple periods, this study suggests that sediment delivery rate decreased each year and was affected by heavy rainfall during each period for the Meiyuan Shan landslide area. Multi-period aerial photogrammetry and ALS can be effectively applied after the landslide disaster for monitoring the terrain changes of the downstream river channel and their potential impacts. |
| first_indexed | 2024-12-20T23:18:02Z |
| format | Article |
| id | doaj.art-bbbb86fc41df4cc1a4108ad0290e2d69 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T23:18:02Z |
| publishDate | 2016-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bbbb86fc41df4cc1a4108ad0290e2d692022-12-21T19:23:35ZengMDPI AGRemote Sensing2072-42922016-02-018319910.3390/rs8030199rs8030199Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in TaiwanYu-Chung Hsieh0Yu-Chang Chan1Jyr-Ching Hu2Central Geological Survey, MOEA, Taipei 235, TaiwanInstitute of Earth Sciences, Academia Sinica, Taipei 115, TaiwanDepartment of Geosciences, National Taiwan University, Taipei 106, TaiwanIn this study, six different periods of digital terrain model (DTM) data obtained from various flight vehicles by using the techniques of aerial photogrammetry, airborne LiDAR (ALS), and unmanned aerial vehicles (UAV) were adopted to discuss the errors and applications of these techniques. Error estimation provides critical information for DTM data users. This study conducted error estimation from the perspective of general users for mountain/forest areas with poor traffic accessibility using limited information, including error reports obtained from the data generation process and comparison errors of terrain elevations. Our results suggested that the precision of the DTM data generated in this work using different aircrafts and generation techniques is suitable for landslide analysis. Especially in mountainous and densely vegetated areas, data generated by ALS can be used as a benchmark to solve the problem of insufficient control points. Based on DEM differencing of multiple periods, this study suggests that sediment delivery rate decreased each year and was affected by heavy rainfall during each period for the Meiyuan Shan landslide area. Multi-period aerial photogrammetry and ALS can be effectively applied after the landslide disaster for monitoring the terrain changes of the downstream river channel and their potential impacts.http://www.mdpi.com/2072-4292/8/3/199Airborne LiDAR (ALS)unmanned aerial vehicles (UAV)photogrammetrydigital elevation model (DEM) differencingswath profile |
| spellingShingle | Yu-Chung Hsieh Yu-Chang Chan Jyr-Ching Hu Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan Remote Sensing Airborne LiDAR (ALS) unmanned aerial vehicles (UAV) photogrammetry digital elevation model (DEM) differencing swath profile |
| title | Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan |
| title_full | Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan |
| title_fullStr | Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan |
| title_full_unstemmed | Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan |
| title_short | Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan |
| title_sort | digital elevation model differencing and error estimation from multiple sources a case study from the meiyuan shan landslide in taiwan |
| topic | Airborne LiDAR (ALS) unmanned aerial vehicles (UAV) photogrammetry digital elevation model (DEM) differencing swath profile |
| url | http://www.mdpi.com/2072-4292/8/3/199 |
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