Document Type : Original Article
Subjects
1- Introduction
Thermal satellite images have low spatial resolution compared to optical images, which are not suitable for evaluating urban land use. Therefore, it is necessary to increase the spatial resolution of these images. Agam et al. (2007) and Hardie and Eismann (2004) used the T Sharp Dis Trade algorithm and vegetation indices to estimate thermal bands with appropriate spatial resolution. Nuo et al. (2021) prepared 30 30-meter land surface temperature map by reducing the image scale using the Random Forest algorithm. The general goal of this research is to investigate the efficiency of three T Sharp Dis Trade, Split Window, and SFIM algorithms point by point in horizontal profiles drawn in Region 4 of Ahvaz and to select the best existing algorithm to achieve a high spatial resolution of thermal images for evaluation of land surface temperature changes.
2- Materials and methods
In this research, a Landsat 8 image dated August 28, 2021, was used. Meteorological data were used to assess the results accurately. T Sharp Dis Trade, Split Window, and smoothing filter-based intensity modulation (SFIM) algorithms were used. SFIM uses a ratio between panchromatic and low-pass filter images to modulate low-resolution multispectral images. A split window algorithm was used to calculate land surface temperature from the thermal data of the TIRS sensor. T Sharp Dis Trade was calculated to improve the spatial resolution of thermal images using vegetation indices. Then, in the T Sharp Dis Trade chart, the output of the profiles obtained from SFIM and Split Window methods was evaluated. Finally, in order to find a relationship between land use and surface temperature, field visits were used to assess the accuracy of temperature differences of these three methods.
3- Discussion and results
After obtaining brightness temperature and NDVI, 30 and 100-meter versions were fused, and a 30-meter image was produced. By fusion of two input images of NDVI and LST, an image with a spatial resolution of 45 meters was extracted in order to improve spatial resolution using the SFIM method. After performing these steps, outputs of SFIM with a spatial resolution of 45 meters, split window algorithm with a spatial resolution of 100 meters, and T Sharp Dis Trade method with a spatial resolution of 30 meters were obtained. Also, land surface temperature was obtained by implementing these three algorithms. In the next step, places with high temperatures were evaluated. The output profiles of SFIM and Split Window methods were analyzed based on visual observation in the T Sharp Dis Trade chart. In this research, profile number 14, due to the diversity of its uses, including vegetation cover, commercial and industrial use, residential use, the highway, grass field, and horse racing track, have been selected as samples from 30 available profiles for analysis and evaluation. In points 1, 3, 4, 5, 10, 12, 13, and 14 of this profile, T Sharp Dis Trade was more efficient, but in points 2 and 6, T Sharp Dis Trade and SFIM were more efficient. In points 7, 8, 9, 11, and 15, T Sharp Dis Trade and Split Window were more effective.
4- Conclusion
High-quality results with high spatial resolution were obtained, using which the boundary of thermal changes can be seen. Two algorithms, SFIM, and Split Window, have shown an increase in surface temperature in sports land use, but the T Sharp Dis Trade algorithm has been the most effective. In commercial and industrial land uses, T Sharp Dis Trade and SFIM have been the most efficient, and Split Window has shown a decreasing trend for surface temperature. SFIM and Split Window results have shown increasing surface temperature trends in mixed land use of green spaces, and T Sharp Dis Trade was more efficient. SFIM and Split Window showed the highest surface temperature in scattered vegetation, but T Sharp Dis Trade was more effective. T Sharp Dis Trade was the most efficient way to deal with dense vegetation. On horse track, the SFIM method showed the best performance. All three algorithms have had good results in highway and heavy traffic land uses. In general, SFIM in residential areas had a very poor match between surface temperature and this land use, and T Sharp Dis Trade and Split Window were the most efficient. SFIM and Split Windows have provided inappropriate results for investigating heat islands and measuring surface temperature. T Sharp Dis Trade has provided very acceptable results for surface temperature.
The authors thank the Khuzestan Meteorological Bureau and the US Geological Survey for providing the required data. The manuscript did not receive a grant from any organization.
Ethical principles have been fully followed in manuscript preparation. The authors declare no conflict of interest.
