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

Vahid Rahdari,Ghaiumi Mohammadi,Razieh Mosadeghi,Alireza Soffianian,Saeid Pourmanafi

doi:10.5937/22-16620

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.

Highlights

Satellite data are often used to prepare land-use and land-cover maps. (Chrysoulakis et al, 2010; Lakshmi et al, 2015)
The objective of this study is to develop a hybrid classification method to prepare accurate land-use/ cover maps even when imageries with lower radiometric resolutions are used
land-use and land-cover (LULC) spectral profiles have shown LULCs digital numbers were more separated in Operational Land Imager (OLI) with 16-bit than Thematic Mapper (TM) data, so this is the reason for the less accuracy in TM map with 8-bit radiometric resolution (Figures 3a and 3b)

Summary

Introduction

Satellite data are often used to prepare land-use and land-cover maps. (Chrysoulakis et al, 2010; Lakshmi et al, 2015). Satellite data are often used to prepare land-use and land-cover maps. Selection of proper land-use classification method is crucial in many inventories especially in watershed’s uplands, which are usually water sources for wetlands (Anderson, 1976; Purkis et al, 2006; Mie et al, 2015; Tian et al, 2015). When satellite images data are used to produce LULC map, it is often very difficult to identify spectrally unique land-use/cover classes because of similar spectral responses arising from different features (Roy et al, 2014; Knudbya et al, 2014; Estoque & Murayama, 2015; Lakshmi et al, 2015). Several methods can be employed to produce LULC by employing remote sensing data (Purkis & Klemas, 2011; Lakshmi et al, 2015; Al-doski et al, 2013). Using low radiometric resolution imageries, land classification can be a serious challenge because of spectral mixing of different surface elements and landscape complexity (Julien et al, 2011; Stenzel et al, 2016)

Objectives

Methods

Results

Conclusion