Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

András Gudmann,Nándor Csikós,László Mucsi,Péter Szilassi

doi:10.3390/rs12213580

Abstract

The use of an object-based image analysis (OBIA) method has recently become quite common for classifying high-resolution remote-sensed images. However, despite OBIA’s segmentation being equally useful for analysing medium-resolution images, it is not used for them as often. This study aims to analyse the effect of landscape metrics that have not yet been used in image classification to provide additional information for land cover mapping to improve the thematic accuracy of satellite image-based land cover mapping. To this end, multispectral satellite images taken by Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) during three different seasons in 2017 were analysed. The images were segmented, and based on these segments, four patch-level landscape metrics (mean patch size, total edge, mean shape index and fractal dimension) were calculated. A random forest classifier was applied for classification, and the Coordination of Information on the Environment Land Cover (CLC) 2018 database was used as reference data. According to the results, landscape metrics both with and without segmentation can significantly improve the overall accuracy of the classification over classification based on spectral values. The highest overall accuracy was achieved using all data (i.e., spectral values, segmentation, and metrics).

Highlights

The monitoring of land use and land cover (LULC) plays a vital role in the study of environmental change, management of natural resources [1,2], urban planning, urban growth modelling [3,4] and creation of environmental policies for sustainable development [5,6,7]
This study aims to analyse the effect of landscape metrics that have not yet been used in image classification to provide additional information for land cover mapping to improve the thematic accuracy of satellite image-based land cover mapping
A greater degree of improvement was achieved in the well-fragmented study area (Gödölloi-hills), where the increase was between 3.98% and 4.21% (Table 3)

Summary

Introduction

The monitoring of land use and land cover (LULC) plays a vital role in the study of environmental change, management of natural resources (e.g., water and wastewater management and biomass monitoring) [1,2], urban planning, urban growth modelling [3,4] and creation of environmental policies for sustainable development [5,6,7]. Due to the expansion of artificial areas and an economic growth that is concomitant with increased human needs (which causes extreme environmental stress), changes in LULC are steadily accelerating. Remote sensing is an essential tool for LULC classification because it provides reliable, extensive and high-temporal- and spatial-resolution data. The greatest advantage of these methods is that they are suitable for extremely rapid land cover mapping of remote, uninhabited areas on a large (continental) scale; one drawback to their use is the uncertainty associated with their thematic accuracy. Improvements may be found with attempts to combine applications of different sensor data, use multi-temporal satellite images or involvement of satellite image based indices in the classification [8,9]

Objectives

Results

Discussion

Conclusion