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.

Urban planning and development in harmony with the geosciences

“…Multiple studies have been carried out on land use and land cover (LULC) assessments to promote sustainable urban planning through the use of remote sensing and GIS technologies. A study examined LULC analyses for sustainable urban development in Kütahya, Türkiye [41]. This study resulted in the production of LULC maps through the use of unsupervised learning techniques, GIS software, and the Landsat and Sentinel-2 remote sensing data from 2017 and 2021.…”

“…This study resulted in the production of LULC maps through the use of unsupervised learning techniques, GIS software, and the Landsat and Sentinel-2 remote sensing data from 2017 and 2021. The research looked at the effectiveness of the automated classifications in developing accurate LULC maps for 2017 and 2021 [41]. Another research [42] held in Tehran, Iran, aimed at investigating the urbanization process.…”