Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{156971,
        author = {PRAVEEN.P and M.S.AYYANAGOUDAR and B.S.POLISGOWDAR and B.MAHESHWARA BABU and RAJASHEKHAR, M and S.S.Prakash},
        title = {ACCURACY ASSESSMENT OF CLASSIFIED LAND USE AND LAND COVER STATUS USING ALGORITHMS EMBEDDED IN  ARCGIS SOFTWARE FOR SHIMSHA CATCHMENT, KARNATAKA},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {5},
        pages = {477-481},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156971},
        abstract = {Surface runoff  dynamics and volume of water stored  in water bodies depend on terrain characteristics  of catchment, climate parameters such as precipitation and temperature, besides land use and land cover of the catchment.  Images obtained from remote sensing  techniques were used to classify the land cover of various classes based on spectral characteristics of the pixel and same will be  interpreted and recorded in the attribute table of shape file generated using ArcGIS software. Generated data  with visual interpretation observed with error and hence assessment of accuracy for classifying image is required  to enhance the quality of work carried and classification was compared by using ground control points which was obtained by physical verification in catchment area and  interpreted the results for further studies. User and producer column data were used to create confusion matrix  to study the difference between referenced data and classified data. The results from accuracy assessment showed  using confusion matrix  shows an overall accuracy obtained from the random sampling process for the image of 85.7 per cent. User’s accuracy ranged from 62 to 100 per cent  while producer’s accuracy ranged from 25 to 100 per cent with kappa’s coefficient of 0.80.},
        keywords = {runoff, remote sensing, classification, accuracy and confusion matrix},
        month = {},
        }