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  • A hierarchical approach of hybrid image classification for land use and land cover mapping

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    Author(s)
    Randari, Vahid
    Soffianian, Alireza
    Pourmanafi, Saeid
    Mosadeghi, Razieh
    Mohammadi, Hamid Ghaiumi
    Griffith University Author(s)
    Mosadeghi, Razieh
    Year published
    2018
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    Abstract
    Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC) in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized ...
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    Remote sensing data analysis can provide thematic maps describing land-use and land-cover (LULC) in a short period. Using proper image classification method in an area, is important to overcome the possible limitations of satellite imageries for producing land-use and land-cover maps. In the present study, a hierarchical hybrid image classification method was used to produce LULC maps using Landsat Thematic mapper TM for the year of 1998 and operational land imager OLI for the year of 2016. Images were classified using the proposed hybrid image classification method, vegetation cover crown percentage map from normalized difference vegetation index, Fisher supervised classification and object-based image classification methods. Accuracy assessment results showed that the hybrid classification method produced maps with total accuracy up to 84 percent with kappa statistic value 0.81. Results of this study showed that the proposed classification method worked better with OLI sensor than with TM. Although OLI has a higher radiometric resolution than TM, the produced LULC map using TM is almost accurate like OLI, which is because of LULC definitions and image classification methods used.
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    Journal Title
    International Scientific Journal Geographica Pannonica
    Volume
    22
    Issue
    1
    DOI
    https://doi.org/10.5937/22-16620
    Copyright Statement
    © The Author(s) 2018. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
    Subject
    Other environmental sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/381214
    Collection
    • Journal articles

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