Land Use and Land Cover Detection by Different Classification Systems using Remotely Sensed Data of Kuala Tiga, Tanah Merah Kelantan, Malaysia (Journal of Tropical Resources and Sustainable Science (JTRSS), 2017)
Land use and land cover classification system has been used widely in many applications such as for baseline mapping for Geographic Information System (GIS) input and also target identification for identification of roads, clearings and also land and water interface. The research was conducted in Kuala Tiga, Tanah Merah, Kelantan and the study area covering about 25 km2. The main purpose of this research is to access the possibilities of using remote sensing for the detection of regional land-use change by developing a land cover classification system. Another goal is to compare the accuracy of supervised and unsupervised classification systems by using remote sensing. In this research, both supervised and unsupervised classifications were tested on satellite images of Landsat 7 and 8 in the years 2001 and 2016. As for supervised classification, the satellite images are combined and classified. Information and data from the field and land cover classification are utilized to identify training areas that represent land cover classes. Then, for unsupervised classification, the satellite images are combined and classified by means of unsupervised classification by using an Iterative Self- Organizing Data Analysis Techniques (ISODATA) algorithm. Information and data from the field and land cover classification are utilized to assign the resulting spectral classes to the land cover classes. This research was then comparing the accuracy of two classification systems at dividing the landscape into five classes; built-up land, agricultural land, bare soil, forest land, water bodies. Overall accuracies for unsupervised classification are 36.34 % for 2016 and 51.76% for 2001 while for supervised classification, accuracy assessments are 95.59 % for 2016 and 96.29 % for 2001.
Keywords: accuracy; landuse and land change; remote sensing; supervised; unsupervised classification