PERFORMANCE ASSESSMENT OF LANDSAT 8 AND SENTINEL-2 SATELLITE IMAGES FOR THE PRODUCTION OF TIME SERIES LAND USE/LAND COVER (LULC) MAPS

Abstract

Land use/Land cover (LULC) maps are essential tools used in various disciplines, including geosciences, urban and regional planning, climate, and agriculture. LULC maps provide a visual representation of the Earth's surface, depicting the different types of land use and land cover in a given area. Land use refers to the human activities that take place on the land, such as agriculture, urban development, and mining, while land cover refers to the physical characteristics of the land, such as forests, grasslands, and wetlands. Researchers can gain insights into environmental trends and patterns, such as deforestation, urbanization, and climate change by analysing changes in LULC over time. While Landsat 8 images have been used to create LULC maps for years, the high-resolution images provided by Sentinel-2 since 2017 have allowed for the creation of highly detailed LULC maps. However, it is still necessary to use Landsat 8 images to produce LULC maps for time-series analyses and future predictions. Unsupervised classification is a method used to create LULC maps using Landsat 8 images, but this study found that the resulting maps differed from those created using Sentinel-2 images, with up to a two-fold difference in the classification of classes such as "Bare Ground," "Built Area," "Crops," and "Trees". Especially when using Landsat data, it is suggested that it would be useful to make evaluations for wider areas/regions as the resolution of Landsat 8 satellite images is limited to 30 meters.