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Remote Sens. 2016, 8(3), 199; doi:10.3390/rs8030199

Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan

1
Central Geological Survey, MOEA, Taipei 235, Taiwan
2
Department of Geosciences, National Taiwan University, Taipei 106, Taiwan
3
Institute of Earth Sciences, Academia Sinica, Taipei 115, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Zhenhong Li, Roberto Tomas, Zhong Lu and Prasad S. Thenkabail
Received: 14 January 2016 / Revised: 19 February 2016 / Accepted: 24 February 2016 / Published: 29 February 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
View Full-Text   |   Download PDF [7627 KB, uploaded 15 March 2016]   |  

Abstract

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. View Full-Text
Keywords: Airborne LiDAR (ALS); unmanned aerial vehicles (UAV); photogrammetry; digital elevation model (DEM) differencing; swath profile Airborne LiDAR (ALS); unmanned aerial vehicles (UAV); photogrammetry; digital elevation model (DEM) differencing; swath profile
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hsieh, Y.-C.; Chan, Y.-C.; Hu, J.-C. Digital Elevation Model Differencing and Error Estimation from Multiple Sources: A Case Study from the Meiyuan Shan Landslide in Taiwan. Remote Sens. 2016, 8, 199.

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