Comparative Assessment of the Unsupervised Land Use Classification by Using Proprietary GIS and Open Source Software

Abstract

Mapping and investigating land use land cover (LULC) changes over a particular region is crucial for resource management, sustainability development, and holistic planning. An increasing rate of urban growth and urban sprawl could induce changes in land use as well as land transformation. However, accurate and up-to-date information about LULC is required for providing better understanding and assessing the environmental consequences of such changes. In this study, the 2017 image from the Sentinel-2A Satellite was utilized to demonstrate the land cover classification analysis in Iskandar Malaysia. Usually, land use classification analysis is conducted through proprietary GIS software. However, this decade shows the advancement in software development, thus the emerging of free/open source software in the geospatial world. Hence, to execute land cover analysis using the Unsupervised Classification technique, the proprietary GIS software (ArcGIS) and free/open source software (QGIS) were deployed. Then, the examination of accuracy assessment was carried out for the selected software. The sum of 250 random points was established for the assessment purpose. The results showed the overall accuracy for ArcGIS and QGIS were 82.80% and 80.40% respectively. The kappa coefficient for ArcGIS was 0.7395, while kappa coefficient for QGIS was 0.7094. Besides that, ArcGIS demonstrated better producer's accuracy in the forest and agriculture land covers classification. Meanwhile, QGIS exhibited better producer's accuracy in the built-up and water features classification. To summarize, ArcGIS and QGIS software are reliable to be used in the land cover classification.