Accuracy Assessment of Unsupervised Land Use and Land Cover Classification Using Remote Sensing and Geographical Information Systems

  • M. D. K. L. Gunathilaka University of Colombo
  • S. L. J. Fernando University of Ruhuna
Keywords: Accuracy, Digital Elevation Model, Geographic Information Systems, Geo-statistics, Kappa Statistics, Remote Sensing


A significant tool for determining how accurate a categorization product is the accuracy assessment. Remote Sensing is one of the essential tools for compiling land use and land cover maps through image classification. The availability of high-quality Landsat imagery and secondary data, an accurate classification technique, and the user's knowledge and competence with the procedures essential to the image classification process. Assess the satellite image classification suitability for further mapping and analysis through accuracy assessments. This paper examined land use and land cover classification using unsupervised classification and extracted NDBI and NDVI further to support main land use and land cover types in the area. The accuracy of classifications was assessed using an error matrix and Kappa statistics. Land use and land cover, NDBI, and NDVI classification accuracies are almost perfect, further verified by the Kappa statistics tool. An excellent unsupervised classification of land use and land cover classes was generated. Accuracy assessment evaluation is one of the most significant tools for determining a classification product's accuracy. The confusion error matrix and Kappa coefficient were particularly useful in calculating accuracy assessment. Typically, accuracy measures of the unsupervised classification show a moderate accuracy level. This study observed almost perfect agreement in all types of accuracy measures. This study is an important source of information that planners and decision-makers may utilize to plan the environment sustainably.


T. Dzurume, T. Dube, K. H. Thamaga, C. Shoko, and D. Mazvimavi, “Use of multispectral satellite data to assess impacts of land management practices on wetlands in the Limpopo Transfrontier River Basin, South Africa,” South African Geogr. J., vol. 104, no. 2, pp. 193–212, 2022.

B. Basudeb, Remote Sensing and GIS. New Delhi, India: Oxford University Press, 2011.

U.S. Geological Survey (USGS), “The Spatial Data Transfer Standard.” United States Geological Survey, 1990.

I. Lucas, F. Janssen, and F. J. van der Wel, “Accuracy assessment of satellite derived landcover data: A review,” Photogramm. Eng. Remote Sens, vol. 60, pp. 426–479, 1994.

S. Aronoff, “Classification accuracy: a user approach,” Photogramm. Eng. Remote Sensing, vol. 48, no. 8, pp. 1299–1307, 1982.

M. E. Ginevan, “Testing land-use map accuracy: another look,” Photogramm. Eng. Remote Sensing, vol. 45, no. 10, pp. 1371–1377, 1979.

R. G. Congalton, “A comparison of sampling schemes used in generating error matrices for assessing the accuracy of maps generated from remotely sensed data,” Photogramm. Eng. Remote Sensing, 1988.

R. G. Congalton, “A review of assessing the accuracy of classifications of remotely sensed data,” Remote Sens. Environ., vol. 37, no. 1, pp. 35–46, 1991.

J. van Vliet, A. K. Bregt, and A. Hagen-Zanker, “Revisiting Kappa to account for change in the accuracy assessment of land-use change models,” Ecol. Modell., vol. 222, no. 8, pp. 1367–1375, 2011.

M. F. Jasinski, “Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale,” Remote Sens. Environ., vol. 32, no. 2–3, pp. 169–187, 1990.

W. B. Cohen, T. A. Spies, and M. Fiorella, “Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA,” Int. J. Remote Sens., vol. 16, no. 4, pp. 721–746, 1995.

Y. Zhang, J. Gao, L. Liu, Z. Wang, M. Ding, and X. Yang, “NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas,” Glob. Planet. Change, vol. 108, pp. 139–148, 2013.

M. D. K. L. Gunathilaka, “Land use and land cover changes and Avifauna: an empirical analysis of loss of agricultural wetlands and its impact on avian species in suburban areas,” Int. J. Sci. Res. Publ., vol. 10, no. 5, pp. 263–272, 2020.

F. Foussenia, H. H. Guoa, Z. X. Haia, J. L. Seburanga, S. A.-S. Mande, and A. Koffi, “Urban area vegetation changing assessment over the last 20 years based on NDVI,” Energy Procedia, vol. 11, pp. 2449–2454, 2011.

M. N. Haque, S. R. Morshed, M. A. Fattah, A. K. Ishra, and M. Saroar, “Environmental Risk Zone Identification of an Urban Unit Using GIS and Remote Sensing,” BAUET J., vol. 2, no. 2, pp. 25–39, 2020.

A.-A. Kafy, M. S. Rahman, M. M. Hasan, and M. Islam, “Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh,” Remote Sens. Appl. Soc. Environ., vol. 18, p. 100314, 2020.

S. S. Rwanga and J. M. Ndambuki, “Accuracy assessment of land use/land cover classification using remote sensing and GIS,” Int. J. Geosci., vol. 8, no. 04, p. 611, 2017.

P. Ukrainski, “Classification accuracy assessment. Confusion matrix method,” 50 degrees North, 2019. (accessed Feb. 10, 2020).

How to Cite
M. D. K. L. Gunathilaka and S. L. J. Fernando, “Accuracy Assessment of Unsupervised Land Use and Land Cover Classification Using Remote Sensing and Geographical Information Systems”, Int. J. Environ. Eng. Educ., vol. 4, no. 3, pp. 76-82, Dec. 2022.
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