Detecting land use land cover using supervised maximum likelihood algorithm on spatiotemporal imagery in Samarinda, Indonesia

Abstract

Land use change commonly has an impact on urban areas. The land use and land cover (LULC) model should be used to address land problems. Land conversion is inextricably linked to Samarinda, Indonesia, the administrative center of the province of East Kalimantan. Additionally, this city serves as the new capital city's Ibu Kota Nusantara-specific buffer zone. The current study aims to find and assess Samarinda City's LULC. A supervised maximum likelihood technique was used to extract this data from spatiotemporal images. Both spatial and non-spatial data about changes in LULC are used in this study. The imagery data for the Samarinda region includes Landsat 5, 7, and 9 images from 1994 to 2022, along with administrative maps and GPS measurement data enabling on-the-ground checkpoints. Before classification analysis, radiometric and atmospheric correction, cropping, and layer stacking procedures were used to treat the image data. In supervised classification, the maximum likelihood method is applied to the four land classes of uncultivated vegetation, cultivated vegetation, waterbodies, and land with buildings. The research result indicates that the number of uncultivated land classes decreased by 6.38 percent and the percentage of cultivated land decreased by 5.52 percent, according to the study's findings. On the other hand, the proportion of water bodies increased by 2.39 percent, and the proportion of built-up land classes increased by 9.51 percent. The overall and kappa accuracy test's average value is 97.67%, higher than the required minimum of 75%.