Geospatial Data Acquisition Using Unmanned Aerial Systems (Uas) A Paradigm for Mapping the Built Environment of the Niger Delta Region of Nigeria
The Rivers State University campus in Portharcourt is one of the university campuses in the city of Portharcourt, Nigeria covering over 21 square kilometers and housing a variety of academic, residential, administrative and other support buildings. The University Campus has seen significant transformation in recent years, including the rehabilitation of old facilities, the construction of new academic facilities and the most recent update on the creation of new collages, faculties and departments. The current view of the transformations done within the University Campus is missing from several available maps of the university. Numerous facilities have been constructed on the University Campus that are not represented on these maps as well as the qualities associated with these facilities. Existing information on the various landscapes on the map is outdated and it needs to be streamlined in light of recent changes to the University's facilities and departments. This research article aims to demonstrate the effectiveness of unmanned aerial systems (UAS) in geospatial data collection for physical planning and mapping of infrastructures at the Rivers State University Port Harcourt campus by developing a UAS-based digital map and tour guide for RSU's main campus covering all collages, faculties and departments and this offers visitors, staff and students with location and attribute information within the campus.
Methodologically, Unmanned Aerial Vehicles were deployed to obtain current visible images of the campus following the growth and increasing infrastructural development. At a flying height of 76.2m (250 ft), a DJI Phantom 4 Pro UAS equipped with a 20-megapixel visible camera was flown around the campus, generating imagery with 1.69cm spatial resolution per pixel. To obtain 3D modeling capabilities, visible imagery was acquired using the flight-planning software DroneDeploy with a near nadir angle and 75 percent front and side overlap.
Vertical positions were linked to the World Geodetic System 1984 and horizontal positions to the 1984 World Geodetic Datum universal transverse Mercator (UTM) (WGS 84). To match the UAS data, GCPs were transformed to UTM zone 32 north.
Finally, dense point clouds, DSM, and an orthomosaic which is a geometrically corrected aerial image that provides an accurate representation of an area and can be used to determine true distances, were among the UAS-derived deliverables.
Keywords; UAS, Geospatial, Acquisition, Orthophoto, Mosaic, Flying –Height.
Full text article
Agisoft, 2019: Agisoft Metashape user manual: Professional edition version 1.5.AgisoftLLCDoc., 145 pp., https://www.agisoft.com/ pdf/metashape- pro_1_5_en.pdf.
Ahmed, N. S., 2016: Field observations and computer modeling of tornado-terrain interaction and its effects on tornado damage and path. Ph.D. dissertation, The University of Arkansas, 247 pp., http://scholarworks.uark.edu/etd/1463.
Bosart, L. F., A. Seimon, K. D. LaPenta, and M. J. Dickinson, 2006: Supercell tornadogenesis over complex terrain: The Great Barrington, Massachusetts, tornado on 29 May 1995. Wea. Forecasting, 21, 897–922, https://doi.org/10.1175/WAF957.1.
Carlson, T. N., and D. A. Ripley, 1997: On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ., 62, 241–252, https://doi.org/10.1016/S0034-4257(97)00104-1.
Carrivick, J. L., M. W. Smith, and D. J. Quincey, 2016: Structure from Motion in the Geosciences. John Wiley and Sons, 197 pp.
Chen, Q. J., Y. R. He, T. T. He, and W. J. Fu, 2020: The typhoon disaster analysis emergency response system based on UAV 2832 MONTHLY WEATHER REVIEW VOLUME 149 remote sensing technology. Int. Arch. Photogramm. Remote Sens. Spat. Info. Sci., XLII-3/W10, 959–965, https://doi.org/ 10.5194/isprs-archives-XLII- 3-W10-959-2020.
Coleman, T. A., 2010: The effects of topography and friction on mesocyclones and tornadoes. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc, P8.12, https://ams.confex.com/ams/25SLS/techprogram/paper_176240.htm.
DiCiccio, T. J., and J. P. Romano, 1990: Nonparametric confidence limits by resampling methods and least favorable families. Int. Stat. Rev., 58, 59–76, https://doi.org/10.2307/1403474.
Du, M., and N. Noguchi, 2017: Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sens., 9, 289, https://doi.org/10.3390/rs9030289.
Efron, B., 1981: Nonparametric standard errors and confidence intervals. Can. J. Stat., 9, 139–158, https://doi.org/10.2307/ 3314608, and
Ezequiel, C. A. F., and Coauthors, 2014: UAV aerial imaging applications for post- disaster assessment, environmental managementand infrastructure development. Int. Conf. on Unmanned Aircraft Systems, Orlando, FL, IEEE, 274–283.
Heredia, G., F. Caballero, I. Maza, L. Merino, A. Viguria, and A. Ollero, 2009: Multi- unmanned aerial vehicle (UAV) cooperative fault detection employing differential global positioning (DGPS), inertial and vision sensors. Sensors, 9, 7566–7579.
Hesterberg, T. C., 1999: Bootstrap tilting confidence intervals and hypothesis tests. Comput. Sci. Stat., 31, 389–393.
Lyza, A. W., and K. R. Knupp, 2014: An observational analysis of potential terrain influences on tornado behavior. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 11A.1A, https://ams.confex.com/ams/27SLS/ webprogram/Paper255844.html.
Smith, M. S., 2006: Exploring local ‘‘tornado alleys’’ for predictive environmental parameters. Proc. 2006 ESRI Int. User Conf., ESRI, 28 pp., https://proceedings.esri.com/library/ userconf/proc06/papers/papers/pap_1339.
Skow, K. D., and C. Cogil, 2017: High-Resolution aerial survey and radar analysis of quasi-linear convective system surface vortex damage paths from 31 August 2014.Wea. Forecasting, 32, 441–467,https://doi.org/10.1175/WAF-D- 16-0136.1.
Tmu_sic´, G., and Coauthors, 2020: Current practices in UAS-based environmental monitoring. Remote Sens., 12, 1001, https:// doi.org/10.3390/rs12061001.
Wagner,M., andR.K.Doe, 2017: UsingUnmannedAerialVehicles (UAVs) to model tornado impacts. 2017 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, AbstractNH31A-0198.
Johnson, Z. Chen, J. Das, and R. S. Cerveny, 2019: Unpiloted aerial systems (UASs) application for tornado damage surveys: Benefits and procedures. Bull. Amer. Meteor. Soc., 100, 2405–2409, https://doi.org/10.1175/ BAMS-D-19-0124.1.
Wang, X., M. Wang, S. Wang, and Y. Wu, 2015: Extraction of vegetation information from visible unmanned aerial vehicle images. Nongye Gongcheng Xuebao, 31, 152– 159, https://doi.org/10.3969/j.issn.1002- 6819.2015.05.022.
Copyright (c) 2022 International Journal of Environmental Science & Sustainable Development
This work is licensed under a Creative Commons Attribution 4.0 International License.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons Attribution 4.0 License or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution: other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
With the understanding that the above condition can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher's final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher's request, the Author agrees to furnish promptly to Publisher, at the Author's own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- The Work is the Author's original work;
- The Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- The Work is not pending review or under consideration by another publisher;
- The Work has not previously been published;
- The Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- The Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author's breach of the representations and warranties contained in Paragraph 7 above, as well as any claim or proceeding relating to Publisher's use and publication of any content contained in the Work, including third-party content.
This work is licensed under a Creative Commons Attribution 4.0 International License.