Geospatial Data Acquisition Using Unmanned Aerial Systems (Uas) A Paradigm for Mapping the Built Environment of the Niger Delta Region of Nigeria
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Abstract
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 Existing information on the various landscapes on the map is outdated and it needs to be streamlined in light of recent changes to the University's facilities and departments.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.
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References
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.
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