Application of Unmanned Aerial System in Shoreline Mapping

References

[1] Anita, S. M., Joaquim, J. S., Timothy, A. W., Ana, C. T., Emanuel, P., Jose, A., Goncalves, J., Delgado, G., Ricardo, B., Stuart, P. and Amy, W. (2018), Unmanned Aerial Systems (UAS) for environmental applications special issue preface, International Journal of Remote Sensing, 39:15-16, pp. 4845-4851.

[2] Ambrosia, V. G., Wegener, S. S., Sullivan, D. V., Buechel, S. W., Dunagan, S. E., Brass, J. A., Stoneburner, J. and Schoenung, S. M. (2003), Demonstrating UAV acquired real-time thermal data over fires. Photogramm. Eng. Remote Sensing, (69), pp. 392–401.

[3] Blakeslee, R. J., Croskey, C. L., Desch, M. D., Farrell, W. M., Goldberg, R. A., Houser, J. G., Kim, H. S., Mach, D. M., Mitchell, J. D. and Stoneburner, J. C. (2003), The Altus Cumulus Electrification Study (ACES): A UAV- Based Science Demonstration. Proceedings of International Conference on Atmospheric Electricity, Versailles, France, p. 1.

[4] Bulletin of Defense Research and Development Organization (2010), Unmanned Aircraft Systems and Technologies, Vol.18 No. 6 December 2010, ISSN: 0971- 4413, pp. 1-20.

[5] Federal Aviation Administration (2007), Unmanned Aircraft Operations in the National Airspace System; Federal Register: Washington, DC, USA, 2007; Volume 72, pp. 6689–6690.

[6] Gbiri, I. A., Idoko, I. A., Okegbola, M. O. and Oyelakin, L. O. (2019), Analysis of Forest Vegetal Characteristics of Akure Forest Reserve from Optical Imageries and Unmanned Aerial Vehicle Data, European Journal of Engineering Research and Science Vol. 4, No. 6, DOI: http://dx.doi.org/10.24018/ejers.2019.4.6.1340, p. 57.

[7] Gitelson, A. A, Stark, R., Grits, U., Rundquist, D., Kaufman, Y. and Derry, D. (2002), Vegetation and Soil lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction, International Journal of Remote Sensing, Vol. 23 No 13, DOI: 10.1080/01431160110107806, pp 2537-2560

[8] Hamuda, E., Glavin, M., and Jones, E. (2016), A Survey of Image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125(c), pp. 185-187.

[9] Hunt, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S. T. and McCarty, G. W. (2010), Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring, Journal of Remote Sensing, 2, pp. 290–305.

[10] Jakub, K. (2018): 3D UAS Mapping of a Copper Mine, GIM International Magazine, Issue 6, Volume 32, p. 30.

[11] Jitka, K. and Pavel, S. (2018), UAV Spectral Image Mapping of Shoreline Vegetation, GIM International Magazine, Issue 6, Volume 32, pp. 26-27.

[12] Meyer, G. E. and Neto, J. C. (2008), Verification of color vegetation indices for automated crop imaging applications, Computers and Electronics in Agriculture, 63(2), pp. 282-290.

[13] Mohammed, R., Ali, R., Muhammad, T., Kang-Hyun, N., and Sung-Ho, K. (2018): Autonomous Vision-based Target Detection and Safe Landing for UAV, International Journal of Control, Automation and Systems 16(6), pp. 3013-3025

[14] Okegbola, M. O., Ajisafe, B. I., Olaosegba, S. O. and Okegbola, S. A. (2019). UAV Image Mapping: An Application in Monitoring and Control of Crime and Insecurity, Global Journal of Engineering Science and Research Management, 6(11), ISSN 2349-4506, DOI: 10.5281/zenodo.3559660, pp. 20-21.

[15] Olagoke, D., James, O., Oluibukun, G. A. and Oladayo, O. (2017). Assessing the Geometric accuracy of UAV-based orthophotos, South African Journal of Geomatics, Vol. 6, No 3, DOI: http://dx.doi.org/10.4314/sajg.v6i3.9, pp. 395-397.

[16] Ponti, M. P. (2013), Segmentation of Low-Cost Remote Sensing Images Combining Vegetation Indices and Mean Shift, IEEE Geoscience and Remote Sensing Letters, 10(1), pp. 67-70.

[17] Perry, J. H., Mohamed, A. El-Rahman, A. H., Bowman, W. S., Kaddoura, Y. O. and Watts, A. C. (2008), Precision Directly Georeferenced Unmanned Aerial Remote Sensing System: Performance Evaluation. In Proceedings of the Institute of Navigation National Technical Meeting, San Diego, CA, USA, pp. 681–686.

[18] Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. and Sorooshian, S. (1994). A modified soil adjusted vegetation index, Remote Sensing of Environment, Vol. 48, Issue 2, pp. 119-126.

[19] Watts, A. C., Ambrosia, V. G. and Hinkley, E. A. (2012), Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use, Journal of Remote Sensing, 4, ISSN 2072-4292, pp. 1672 -1674.

@article{1704465,
      author = {Michael O. Okegbola, Ganiyu O. Raheem, Mutairu A. Yusuf, Latifat O. Oyelakin, Ayodele Oduwole; Somtoochukwu C. Okafor},
      title = {Application of Unmanned Aerial System in Shoreline Mapping},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {479-486},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704465.pdf},
      abstract = {Unmanned Aerial System (UAS) is an aspect of close range remote sensing and photogrammetry method that provides excellent and timely spectral and spatial services for rapid identification of details due to the low altitude of flight and identification of a clear water surface, topographic mapping and analysis, vegetation monitoring, land use change analysis, shorelines and river bank level monitoring, etc. Therefore, while comparing the imperative costs of the UAS and its major components (UAV, Ground Controller and Radio Communication) with the cost of multispectral and thermal cameras which are still very expensive alongside their end products as compared to visible cameras of UAV and the end products for cost effective and maximal applications in environmental surveying and modelling. However, this research shows how a commercially available middle-classed Quadcopter UAV/DRONE which is affordable even for the individuals to acquire was flown over the study area in accordance to pre-planned flight in Drone Deploy to collect data inform of images only in the visible region of the spectral bands i.e RGB, these images were then processed and mosaicked to obtain the Orthophoto using the Agisoft Metashape. In ArcMap, the orthophoto was used to calculate and generate different spectral vegetation indices. These indices were further visualized, analyzed and interpreted for different applications as it relates to identification and mapping of shoreline and vegetation including the clear water and the results includes NDI, CIVE, VDVI, VARI etc. which clearly shows the Shoreline, clear water and the vegetation better than the true colour image.},
      keywords = {DRONE, Mapping, Remote Sensing and Photogrammetry, Spectral Image, UAS.},
      month = {May},
  }