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

Vahid Rahdari; Alireza Soffianian; Saeid Pourmanafi; Razieh Mosadeghi; Hamid Ghaiumi Mohammadi

doi:10.5937/gp22-16620

Vahid Rahdari Natural Resource Faculty, Univeristy of Zabol, Iran
Alireza Soffianian Natural Resource Faculty, Isfahan University of Technology, Isfahan, Iran
Saeid Pourmanafi Natural Resource Faculty, Isfahan University of Technology, Isfahan, Iran
Razieh Mosadeghi Griffith Centre for Coastal Management, Griffith University, QLD, Australia
Hamid Ghaiumi Mohammadi Soil & Geomorphology in Iranian Soil and Water Research Institute, Isfahan, Iran

DOI: https://doi.org/10.5937/gp22-16620

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 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.