Research Articles

Google Earth imagery coupled with on-screen digitization for urban land use mapping: case study of Hambantota, Sri Lanka

Authors:

Abstract

Monitoring of land use changes using remote sensing techniques in urban areas is important in appraising urban development and environmental sustainability. However, when considering the cost-effectiveness and mapping accuracy, selection of a proper data source and an image classification technique has become a challenge, especially for researchers in the developing countries. This study, hence, aimed at investigating the effectiveness of two image sources, Google Earth and Landsat8 as well as two classification methods, pixel-based classification and on-screen digitisation, in studying land use changes in Hambantota urban area covering a land area of about 4,000 hectares. Land use maps were created applying the two aforementioned classification techniques on the two open source images in different combinations to select the best option for studying land use changes in smaller urban areas. Results show that the overall accuracy of pixel-based classification for Landsat8 and Google Earth images are 62.6 % and 59.1 %, respectively, whereas on-screen digitisation of Google Earth imagery resulted in the highest overall accuracy of 88.4 %. Therefore, Google Earth images with on-screen digitisation increased the accuracy of the land use map by 25.8 % and 29.3 %, respectively, compared to land use maps created by pixel-based classification of Landsat8 and Google Earth images. Furthermore, classification accuracies of paddy lands and sandy areas were improved by 74 % and 61 %, respectively, when on-screen digitisation method was applied to Google Earth images compared to pixel-based classification of Landsat8 and Google Earth images. Therefore, use of on-screen digitisation method on Google Earth imagery, is recommended as a cost-effective and high accuracy method for land use mapping of smaller urban areas, particularly, in developing countries.  

Keywords:

Accuracy assessmentImage classificationLand use classesGoogle Earth imageryLandsat8 imageryMaximum likelihood methodUrban development
  • Year: 2020
  • Volume: 48 Issue: 4
  • Page/Article: 357-366
  • DOI: 10.4038/jnsfsr.v48i4.9795
  • Published on 31 Dec 2020
  • Peer Reviewed