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2023 | Vol. 24, iss. 9 | 161--169
Tytuł artykułu

Land Use and Land Cover Analysis Using Geomatics Techniques in Amara City

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EN
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EN
The remote sensing technique is crucial for creating maps showing land use and land cover from a procedure known as image classification. For the process of image classification to be successful, many aspects must be taken into consideration; one of these factors is the availability of high-quality Landsat images. This study aims to classify and map the studied area’s land use and cover using remote sensing and geographic information system techniques. This study is divided into two parts: part one focuses on classifying land use and land cover, while part two evaluates how accurate the classification is. Several classification methods are compared for their efficacy in this study. Some image classification methods have shown promising results when used to remote sensing data. An efficient classifier is necessary for extracting data from remote-sensing images. The maximum likelihood classification was the most effective classifier in our study. In this study, the Maximum Likelihood classification accuracy has achieved an overall accuracy of 91% and an overall kappa accuracy of 86.83%. This study provides essential data for planners and decision-makers to design sustainable environments.
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Strony
161--169
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
  • Surveying Department, Technical Institute of Amara, Southern Technical University, Misan, Iraq
  • Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq
Bibliografia
  • 1. Atkinson, P.M., Tatnall, A.R.L. 1997. Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18(4), 699–709.
  • 2. Campbell, J. B., and Wynne, R. H. 2011. Introduction to remote sensing. Guilford press.
  • 3. Disperati, L., and Virdis, S.G.P. 2015. Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon, central Vietnam. Applied Geography, 58, 48–64.
  • 4. Eastman, J.R. 2003. IDRISI Kilimanjaro: guide to GIS and image processing.
  • 5. Falih, K.T., and Saedi, A.S.J. Al. 2020. Evaluating the distribution of health services in Nasiriyah City by utilizing geomatics techniques. IOP Conference Series: Materials Science and Engineering, 928(2), 22062.
  • 6. Jasinski, M.F. 1996. Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 34(3), 804–813.
  • 7. Kadhum, Z.M., Jasim, B.S., Al-Saedi, A.S. J. 2023. Improving the spectral and spatial resolution of satellite image using geomatics techniques. AIP Conference Proceedings, 2776(1).
  • 8. Kadhum, Z.M., Jasim, B.S., Obaid, M.K. 2020. Change detection in city of Hilla during period of 2007-2015 using Remote Sensing Techniques. IOP Conference Series: Materials Science and Engineering, 737(1). https://doi.org/10.1088/1757-899X/737/1/012228
  • 9. Merchant, J.W., and Narumalani, S. 2009. Integrating remote sensing and geographic information systems. Papers in Natural Resources, Paper 216.
  • 10. Moran, E.F., Skole, D.L., Turner, B.L. 2004. The development of the international land-use and landcover change (LUCC) research program and its links to NASA’s Land-Cover and Land-Use Change (LCLUC) Initiative. Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth’s Surface, 1–15.
  • 11. Perumal, K., and Bhaskaran, R. 2010. Supervised classification performance of multispectral images. ArXiv Preprint ArXiv:1002.4046.
  • 12. Richards, J.A., and Jia, X. 2006. Image classification methodologies. Remote Sensing Digital Image Analysis: An Introduction, 295–332.
  • 13. Rwanga, S.S., and Ndambuki, J.M. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611.
  • 14. Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., Rahman, A. 2020. Land-use land-cover classification by machine learning classifiers for satellite observations – A review. Remote Sensing, 12(7), 1135.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
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