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Comparison of DEM Generated from UAV Images and ICESat-1 Elevation Datasets with an Assessment of the Cartographic Potential of UAV-Based Sensor Datasets

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Proceedings of UASG 2021: Wings 4 Sustainability (UASG 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 304))

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Abstract

The availability of Very High-Resolution (VHR) remote sensing datasets from the Unmanned Aerial Vehicle (UAV) based sensors are changing the methods of cartographic mapping as well as visualization by taking advantage of both the high spatial resolution as well as high radiometric resolutions. A high-fidelity digital elevation model (DEM) can be prepared using these UAV datasets, which can produce high-quality orthoimages. In the present study, the space-borne lidar elevation datasets from the Ice, Clouds, and Land Elevation Satellite (ICESat-1) and TanDEM-X 90 m DEM from TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission are utilized for the comparison of elevation values from DEM generated using UAV datasets for the experimental site in Switzerland. The experimental site is part of Yverdon-Les-Bains, which is a municipality in the district of Jura-Nord VauDOIs, canton of Vaud, Switzerland. The openly accessible dataset from the Sensefly Sensor Optimized for Drone Applications (S.O.D.A.) includes 235 true-color RGB images acquired from a flight height of 106 m, at an average Ground Sampling Distance (GSD) of 2.64 cm. The datasets are processed in Pix4D software for the bundle block adjustment, followed by the generation of DEM and orthomosaic. The comparison of ICESat-1 elevation data with DEM depicts a difference of about 26 cm on plain ground, which is reasonably good considering the use of a Global Navigation Satellite System (GNSS) network in Real-Time Kinematic (RTK) mode. The quality report depicts the mean of geolocation accuracy in X, Y, and z as 2.73 cm, 2.73 cm, and 3.46 cm respectively, which is practically highly accurate. Root Mean Square Error (RMSE) in X, Y, and z is computed as 1.7 cm, 2.27 cm, and 2.31 cm respectively. The study depicts that practically the cartographic potential for the UAV dataset is suitable for mapping at a scale range of 1:250 to 1:300 or better for such plain terrain conditions, meeting the engineering drawing requirements for facility management and utility mapping.

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Acknowledgements

The authors thank the National Aeronautics and Space Administration (NASA), German Aerospace Center (DLR), and senseFly Inc. for their web-based portals providing datasets for education and research. The authors are highly thankful to the Director, Indian Institute of Remote Sensing (IIRS) for his support and encouragement of the research activities. The authors are thankful for the creative research effort of the Open Source Geospatial Foundation for providing Quantum GIS to the research community.

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Authors and Affiliations

  1. Photogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing, Dehradun, India

    Ashutosh Bhardwaj

  2. Urban and Regional Studies Department, Indian Institute of Remote Sensing, Dehradun, India

    Surendra Kumar Sharma & Kshama Gupta

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  1. Ashutosh Bhardwaj
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  3. Kshama Gupta
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Correspondence to Ashutosh Bhardwaj .

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Editors and Affiliations

  1. Department of Civil Engineering, Centre of Excellence in Disaster Mitigation and Management (CoEDMM), Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kamal Jain

  2. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Vishal Mishra

  3. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW, Australia

    Biswajeet Pradhan

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Bhardwaj, A., Sharma, S.K., Gupta, K. (2023). Comparison of DEM Generated from UAV Images and ICESat-1 Elevation Datasets with an Assessment of the Cartographic Potential of UAV-Based Sensor Datasets. In: Jain, K., Mishra, V., Pradhan, B. (eds) Proceedings of UASG 2021: Wings 4 Sustainability. UASG 2021. Lecture Notes in Civil Engineering, vol 304. Springer, Cham. https://doi.org/10.1007/978-3-031-19309-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-19309-5_1

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