The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period.

The aims of this study are to detect the land-cover maps and land surface temperatures using Landsat time series and analyse the relation between the land-cover and land surface temperatures (LST) and their changes in time. For these purposes initially, land-cover maps were generated rapidly using land cover indices and automatic thresholding. The land-cover indices used in this study are Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Index-Based Built-Up Index (IBI), Modified Bare-Soil Index (MBI), Plastic-Mulched Landcover Index (PMLI), Plastic Greenhouse Index (PGI) and Normalized Burn Ratio Thermal (NBRT) Index. Then, using the thermal bands of Landsat satellites, LST maps were created. Finally, the land-cover and LST changes were examined. The Kumluca district of Antalya, which includes extensive greenhouse areas as well as urban, vegetation, bareland, water, was selected as study area. Between the years 2004 and 2021, within the study area the greenhouse areas increased significantly, the urban area expanded and some areas exposed to fire, especially the fire in 2016. Therefore, the images within this time period were used. The overall accuracies for land-cover maps were computed as 76%, 79%, 79%, 89% and 86% for the years 2004, 2009, 2013, 2017 and 2021, respectively. The results obtained from the study reveal that while greenhouse and urban areas were increased, the vegetation areas were decreased significantly within this time period. In addition, generally increases were observed for LST values of all land-cover classes and the highest LST values were detected for the burned, bareland, urban and greenhouse areas.

The Mediterranean Region is considered one of the most vulnerable regions to climate change, with annual temperatures projected to increase. With the additional heat load associated with anthropogenic activities, this region is expected to experience greater heat stress and negative health effects than other regions. The Antalya Basin, situated within the Mediterranean Basin, is characterized by rich biodiversity, important wetlands, intensive agricultural and tourism activities, and rapid urbanization, providing a relevant setting for examining land use/land cover (LU/LC) and land surface temperature (LST) relations. This study investigates long-term LU/LC and LST changes in the basin and assesses the relationship between them. For this purpose, initially pre-processing steps were performed to prepare the data sets and spectral indices were generated. Then the LU/LC classification was performed using Landsat imagery and auxiliary data for nine years at five-year intervals from 1985 to 2025. Afterwards the annual LST images were generated using Landsat imagery for the years between 1985 and 2025. Finally, accuracy assessment was performed and the relationship between LU/LC and LST was analyzed. The LU/LC thematic maps derived from Landsat images along with additional raster layers were obtained quite accurately with overall accuracies higher than 85%. The change detection results revealed increases in residential, industrial, and agriculture areas and were accompanied by decreases in vegetation and rangeland/bareland areas. Long-term annual LST analyses revealed distinct thermal trajectories among LU/LC classes from 1985 to 2025. Statistically significant monotonic warming trends were confirmed in residential, industrial, water surfaces, vegetation, and greenhouse areas, with residential areas demonstrating the strongest warming rate. In contrast, dry agriculture, rangeland/bareland, and irrigated agriculture exhibited high interannual variability and pronounced surface temperature peaks—most notably in 2024, when dry agriculture reached a maximum LST of 47.72 °C—but lacked statistically significant long-term monotonic trends. The highest LST values were measured in dry agriculture areas, followed by greenhouse, industrial, and residential areas. In contrast, water and vegetation areas exhibited the lowest LST values among all LU/LC classes.