Examination of the Relationship Between Land Use/Cover Change with Surface Temperature (Case Study of the City of Maragheh in 2000, 2010 and 2020)

In this study, land use and land cover changes in Maragheh County for the years, 2010, and 2020 were investigated using remote sensing and GIS. In, land use and land cover were classified into 7 classes, while in 2010 and 2020, due to the establishment of a glass manufacturing factory and the separation of its wastewater from other water sources, the classes were classified into 8 classes: water, glass factory wastewater, bare land, snow, human-made construction, irrigated agricultural land, and rainfed cultivation. The results indicate an increase or decrease in some of these classes over time. Human-made construction had an area of 31.63 square kilometers in, which increased to 74.75 square kilometers in 2010 and 188.87 square kilometers in 2020, indicating an increase in human construction activities. The glass manufacturing factory, which contains toxic substances, has caused damage to the surrounding farmers in previous years due to the diversion of its channel. In, it did not exist at all, but in 2010, its wastewater area reached 0.94 square kilometers, and in 2020, it expanded to 1.31 square kilometers. The information obtained from detecting land use and land cover changes can provide optimal solutions for selecting, planning, implementing, and monitoring development projects to meet the increasing human demands and effectively manage land resources.

2. Methods and Material
In this study, Landsat 8 OLI sensor images were used for the year 2020, and Landsat 5 TM satellite images were used for the years 2010 and. The images were acquired in the summer season of January (Khordad) and July (Tir) to avoid high cloud and snow cover and to capture the peak growth of vegetation in this region during these months. The supervised classification method and the maximum likelihood algorithm were used for land use and land cover classification for the years, 2010, and 2020. The images were accessed through the powerful Google Earth Engine platform. This platform was chosen because 1) the Landsat 8 and Landsat 5 images in this platform have been atmospherically and radiometrically corrected, 2) instead of downloading an image with approximately 1 GB in size, only the study area image could be downloaded with a smaller size, and 3) it saved time and avoided the difficulties of downloading and the initial corrections of the Landsat images. Additionally, the Pan Sharpening operation for Landsat 8 was also performed using this powerful platform. Then, for pixel-based classification, training samples were collected from various classes, and Google Earth data was also utilized. The maximum likelihood method was employed for classification using ENVI 5.3.1 software. Finally, the output of the classification was transferred to ArcMap 10.8 software to obtain the final results.

3. Results and Discussion
The area of these land uses in square kilometers was plotted on a graph for the study years. The largest class for all three study years was the "bare" class, with areas of 1542.17 square kilometers in 2010, 1577.90 square kilometers in 2010, and 1359.58 square kilometers in 2020. The smallest class was the "wastewater from the glass factory," which did not exist in and had an area of 0.94 square kilometers in 2010 and 1.31 square kilometers in 2020, indicating that as the factory expanded, it occupied more land for wastewater purposes. One of the most important classes in this study was "human settlements." In, the area of this land use was 31.63 square kilometers, accounting for 1.4% of the total area. In 2010, it increased to 74.75 square kilometers, representing 3.49% of the total area, and in 2020, it reached 188.87 square kilometers, equivalent to 8.83% of the total area. This indicates that with population growth and the establishment of factories and industrial estates, the area and percentage of this land use increased from to 2020. Snow coverage in this region also played a role, particularly in the Sofi Chay watershed, where the Alvian Dam is located, as well as in the Mardagh Chay watershed, which flows into Lake Urmia, affecting the region's groundwater and agricultural activities. In, the snow-covered area was 8.69 square kilometers, and in 2010, due to the image being taken in July instead of January, closer to the warm season, it was 37.43 square kilometers, indicating an increase in snowfall in that year. In 2020, the snow-covered area was 26.96 square kilometers. The area of rangelands in was 229.76 square kilometers, as there was no snow cover, and the mountains of Sahand had vegetation. In 2010, the rangeland area decreased to 82.80 square kilometers, as the land use was transformed into snow-covered areas. In 2020, the rangeland area reached 179.91 square kilometers
4. Conclusion
In this study, human-made land use in covered an area of 31.63 square kilometers, equivalent to 1.4% of the total area of Maragheh County. In 2010, this land use expanded to 74.75 square kilometers, accounting for 3.49% of the county's total area. By 2020, the area of human-made land use reached 188.87 square kilometers, representing 8.83% of the total county area. This indicates an upward trend over these 20 years, reflecting population growth and subsequent increase in human construction activities. Another significant change observed in the study area was the glass manufacturing factory. In, the factory itself was present without any wastewater coverage. However, in 2010, the wastewater area accounted for 0.94 square kilometers, equivalent to 0.4% of the study area. By 2020, the wastewater area increased to 1.31 square kilometers, covering 0.06% of the total area. The discharge of this wastewater has caused damage to the agricultural lands surrounding the factory due to its increase and diversion from reservoirs in previous years. The detection of changes through these technologies is made possible with less time, lower costs, and better accuracy. The present study demonstrates that remote sensing and GIS are important technologies for temporal analysis and spatial quantification of phenomena that would not be feasible through traditional mapping techniques.
Keywords: Land use