Vol.3 , No. 1, Publication Date: Jan. 8, 2018, Page: 1-9
[1] | Nzelibe Ifechukwu Ugochukwu, Department of Surveying and Geoinformatics, Federal University of Technology, Akure, Nigeria. |
[2] | Tata Habert, Department of Surveying and Geoinformatics, Federal University of Technology, Akure, Nigeria. |
The issue of encroachment on Right Of Way (ROW) in Nigeria is one which has led to several devastations. Therefore, there is a need for right of way surveys to be carried out more frequently and adequately. Due to the fact that the conventional means of carrying out right of way surveys, though provides a more appropriate solution, has been found to be expensive, time consuming and laborious, there is the need to seek alternative means for carrying out right of way surveys. To achieve this aim, the potentials of high resolution satellite image-QuickBird was evaluated by selecting a 1.4km road corridor upon which feature within the 30m ROW were extracted and coordinated by method of conventional ground survey, using Total station instrument. Both datasets were plotted in same environment using the ArcGIS10.3.1 software and spatial analyses were carried out, which include: Buffering, Overlays, Queries and Computations of areas of encroachment. Based on the results and analysis obtained from both data sources, the mean and standard deviation computed for their absolute differences in area of encroachment were 1.656177 m2 and 0.587613 m2 respectively. The satellite product had plotting accuracy of about 1.51635m against 0.08m from conventional surveys and the project had estimated cost of US $500 against about US $3253 for conventional survey. Evidently, the satellite product provided a quicker, cheaper and less laborious solution in carrying out ROW surveys within a satisfactory level of accuracy.
Keywords
ArcGIS, Encroachment, High Resolution Satellite Image, Right-of-Ways-ROW, QuickBird, Nigeria
Reference
[01] | Baumgartner, A., Steger, C., Mayer, H., Eckstein, W., & Ebner, H. (1999.). Automatic road extraction based on multi-scale, grouping, and context. Photogrammetric Engineering & Remote Sensing, 777-785. |
[02] | Gopalan, A. K. (2009). GIS@development.net. Retrieved June 14, 2017, from High Resolution Imagery for developmental Planning with spatial references to development.: http:/www.GISdevelopmentmet.net/ technology/ rs/ techrsr0014pf.htm. |
[03] | Jensen, J. R., & Cowen, D. C. (1999.). Remote Sensing of Urban Suburban Infrastructure and Socio-Economic Attributes. American Society for Photogrammetry and Remote Sensing, 611-622. |
[04] | Land Info. (2016, May). Satellite Imagery Pricing. Retrieved June 14, 2017, from http://www.landinfo.com/LAND_INFO_Satellite_Imagery_Pricing.pdf |
[05] | Leordeanu, D. C. (2016). Aerial image geolocalization from recognition and matching of roads and intersections. 1-2. |
[06] | Nigerian Institution of Surveyors. (2008). Professional Scale of Fees. Nigeria: Nigerian Institution of Surveyors. |
[07] | Satellite Imaging Corporation. (2017). QuickBird Satellite Sensor. Retrieved June 5, 2017, from http://www.satimagingcorp.com/satellite-sensors/quickbird/ |
[08] | Shaka, F. I., Abd-Elrahman, A., Abdel-Gawad, K. A., & Sherief, A. M. (2011). Building Extraction from High Resolution Space Images in High Density Residential Areas in the Great Cairo Region. Remote sensing, 2-3. |
[09] | Sohn, G., & Dowman, I. (2003). Building Extraction Using LiDAR DEMs and IKONOS Images. In Proceedings of the ISPRS Working Group III/3 Workshop (pp. 8-10), Dresden, Germany: ISPRS. |
[10] | Thomas, N., Hendrix, C., & Congalton, R. (2003). A comparison of urban mapping methods using high-resolution digital imagery. Photogramm. Remote Sensing, 963-972. |
[11] | Urala, S., Shana, b. J., Romeroa, M. A., & Tarko, A. (2015). Road And Roadside Feature Extraction Using Imagery And Lidar Data for Transportation Operation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (pp. 238-239). Munich, Germany. |