Land surface temperature (LST) is controlled by the equilibrium of ground and atmosphere energy, as well as superficial and sub-surface thermal properties, and is considered as an important parameter in many environmental models. Knowing the extent of LST contributes to a wide range of issues related to earth sciences, such as the urban climate, global environmental changes, and the study of human-environment interactions. Land use and land cover information are recognized as an essential and important component of data used in various aspects of regional planning, research on global change, and applications in the field of environmental monitoring. On a global scale, changes in land use / land cover resulted in changes in regional and local temperature regimes. Land use patterns affect LST and can be considered as an indicator for the trend process. Using LST, everyone can find useful information about the physical and physical characteristics of the earth and climate that play a significant role in environmental processes. LST is an important factor in many fields of study such as global climate change, hydrology, agriculture, and land use / land cover.
The aim of this study was the investigating the relationship between surface temperature and vegetation cover and land use in Gorgan plain using remote sensing data. In the first step, the Landsat 8 image of the year 2018 was pre-processed and prepared and the land use / land cover map was prepared in 8 classes. Then, to measure the surface temperature of the thermal bonding of the image and the related equations were used. Finally, the normalized difference vegetation index or NDVI was used to calculate surface mapping and the LST surface temperature map was created. In order to neutralize the effect of altitude on LST, selected pixels from the elevation points were selected in each land use. All LST computational steps and the NDVI index were performed using the ArcGIS10.4.1 software and the 2018 land use was created using the Idrisi software. In this research, the linear regression method was used to obtain the effect of NDVI and its effects on LST. Evaluation of LST extracted from meteorological stations It should be noted that the surface temperature, which indicates the surface heat of the body, is slightly different from that of the air contained in that body. Using the following equation, the air temperature can be obtained from the values of LST:Equation (1)
In order to prove the accuracy of the work for the preparation of the surface temperature map, the temperature values measured by the three synoptic stations (Kordb ku-Blok, Gorgan and Nomal-Dam Kowsar) were compared on the same date with the obtained values of air temperature from the surface temperature values.
The results showed that bare land class has a higher temperature (45.96 ° C) due to lack of protective cover. Since the vegetation is very limited and dispersed in the bare land areas, the Earth is more exposed to solar waves. On the other hand, the surface of the bare solid ground is bright, which affects energy absorption and increases surface temperature. While the use of irrigated agriculture and water resources was 29.95 and 34.33 degrees Celsius, the lowest average temperature was observed among other classes. Considering the time taken to get the image of products cultivated in agriculture, they had an acceptable level of growth and greenness (high NDVI index highlighted the greenery of arable crops on this date) and by influencing evaporation reduction and maintaining soil moisture in effective thermal modification Which have led to less solar heat absorption and eventually reduced temperature. Water resources also reduce the surrounding air due to its high heat capacity and low solar energy absorption. Since forest class is at higher altitudes, its surface temperature was studied separately. The comparison of the surface temperature of the pixels related to the use of forest and the forestry sector showed that the LST in the forestry sector was about 5 degrees Celsius above the forest class. According to the results, the correlation between the NDVI index and the surface temperature is 0.65. The negative correlation obtained between this index and the surface temperature indicates an inverse relationship between this index and the surface temperature, and it can be deduced that in areas with high vegetation density such as forest use, surface temperature is much lower than other uses, which suggests a type of relationship Usage with surface temperature. According to Sig, this correlation is significant at 95% confidence level. Evaluation of surface temperature map prepared with ground data The results of the correlation test between the surface temperature of Landsat 8 and the air temperature of the meteorological station as well as the correlation between the air temperature and the existing stations were both obtained at 0.99, which confirmed the accuracy of equation (1) used to convert the LST data to the data Air temperature. The difference in LST between stations in the area indicates that stations are located in different environmental conditions due to environmental factors such as elevation, slope, direction, distance from the sea on LST.
In this study, to determine the relationship between land use and LST, the surface temperature map of the area was prepared and the surface temperature of the area between 14 and 51 degrees Celsius was estimated. Since the height parameter has an effective effect on temperature, the samples were selected from the height points of each land use. Thus, the effect of height factor on the results of the research was neutralized. In areas where vegetation is dense, such as forest, surface temperatures are far lower than other uses. Also, the irrigated agriculture class, which had a higher density than rangelands, showed lower temperatures. On the other hand, the bare lands had the highest surface temperature. Therefore, it can be concluded that vegetation is a major factor in surface temperature, especially in areas where this coating is denser. The effect is more obvious.
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