IN006
Challenges and Best Practices for Using UAV in Field Research
Session ID#: 160576
Session Description:
Uncrewed Aerial Vehicles (UAV), commonly known as drones, are being routinely applied to capture data in numerous scientific applications. The capabilities of drone-mounted sensors address the critical scale gap between ground- and satellite-based observations by collecting high-resolution data (mm-cm scale) over large surface areas (1–10 km
2 and greater). Their advantage is the ability to deliver near-real-time society-relevant information.
As a new technology, however, there are currently no industry-wide accepted best practices for drone sensor and flight data handling and management. Without common standards, the development of mature tools for drone-captured data processing and fusion with other data sources is currently hampered. As a consequence, each use case generally develops a unique custom pipeline that only sees one-time use.
The aim of this session is to explore and discuss best practices for the management of drone-captured sensor and flight data and bring together the international user and developer communities.
Index Terms:
0394 Instruments and techniques [ATMOSPHERIC COMPOSITION AND STRUCTURE]
0452 Instruments and techniques [BIOGEOSCIENCES]
1912 Data management, preservation, rescue [INFORMATICS]
1930 Data and information governance [INFORMATICS]
Primary Convener: Misha Krassovski, Oak Ridge National Laboratory, Environmental Sciences Division, Oak Ridge, TN, United States
Conveners: Jens F Klump, CSIRO Earth Science Resource Engineering, Perth, Australia and Juri Klusak, Orthodrone GmbH, Kiel, Germany
MICRODRONES IN FIELD-BASED STRUCTURAL GEOLOGY: A PHOTOGRAMMETRY AND FRACTURE QUANTIFICATION CASE STUDY FROM THE NORTH MOUNTAIN BASALT, NOVA SCOTIA, CANADA (1043150)
Alexander Peace, McMaster University, School of Earth, Environment & Society, Hamilton, ON, Canada and Scott Jess, University of Toronto, Toronto, ON, Canada
Unmanned Aircraft System Multispectral Data Calibration and Evaluation (1049382)
Christopher J Jarchow1, Armando Barreto-munoz2, Truman Combs3 and Kamel Didan2, (1)RSI EnTech, LLC, Applied Studies and Technology, Grand Junction, United States, (2)University of Arizona, Biosystems Engineering, Tucson, AZ, United States, (3)University of Arizona, Biosystems Engineering, Tucson, United States
Current Challenges of UAV-Based Hyperspectral Imaging in Agriculture (1068740)
Santosh S Palmate1, Yohtaro Kobayashi2 and Saurav Kumar1, (1)Texas A&M University, Texas A&M AgriLife Research, El Paso, TX, United States, (2)Virginia Tech, Department of Biological Systems Engineering, Blacksburg, VA, United States
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Tiziano Volatili1, Claudio Di Celma1, Alan David Pitts2, Pietro Paolo Pierantoni1, Stefano Mazzoli1 and Tondi Emanuele1,3, (1)University of Camerino, School of Science and Technology, Geology Division, Camerino, Italy, (2)U.S. Geological Survey, Florence Bascom Geoscience Center, Reston, United States, (3)Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Sismologia e Tettonofisica, Camerino, Italy
Geologic Interrogation of 3D Outcrop Reconstructions: Sources of Uncertainty and Mitigation Strategies (1169469)
Adam Cawood1, Nina Bowness1, Clare E. Bond2, David A Ferrill1, Kevin J Smart1 and Brian Burnham2, (1)Southwest Research Institute, San Antonio, United States, (2)University of Aberdeen, School of Geosciences, Aberdeen, United Kingdom
How UAV improve past metallurgical deposits characterization for landfill regeneration (1177216)
Hadrien Michel1,2, Marc Dumont2, David Caterina Sr.2, Itzel Isunza Manrique2, François Jonard3, Tom Debouny2 and Frederic Nguyen2, (1)F.R.S.-FNRS (Fonds de la Recherche Scientifique), Brussels, Belgium, (2)Université de Liège, Urban and Environmental Engineering, Liege, Belgium, (3)Université de Liège, Earth Observation and Ecosystem Modelling Laboratory, Liege, Belgium
Deep learning-based method to identify tree species using UAVs images in the temperate forest. (1193467)
Yunmei Huang1, Kexin Meng2, Botong Ou2, Emily M Griffin3, Songlin Fei4 and Baijian Yang3, (1)Purdue University, West Lafayette, IN, United States, (2)Purdue university, West lafayette, United States, (3)Purdue University, West Lafayette, United States, (4)Purdue University, Forestry and Natural Resources, West Lafayette, United States
Using Multispectral Aerial Platforms to Manage Soybean Cyst Nematode Disease (1195175)
Xian Liu1, Joseph Kalinzi2, Jason Bond2, Ahmad Fakhoury2 and Ruopu Li3, (1)Southern Illinois University Carbondale, Carbondale, IL, United States, (2)Southern Illinois University Carbondale, Carbondale, United States, (3)Southern Illinois University Carbondale, School of Earth Systems and Sustainability, Carbondale, IL, United States
