Shahriar Khaledi; Ghasem Keikhosravi; Farzaneh Ahmadibarati
Abstract
Extended AbstractIntroductionAmong the climatic elements, the effect of temperature in an area and its changes is the perception of land reclamation and can be maintained and land use of a place. Mean while, surface temperature is an important factor in global warming studies and as a representative ...
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Extended AbstractIntroductionAmong the climatic elements, the effect of temperature in an area and its changes is the perception of land reclamation and can be maintained and land use of a place. Mean while, surface temperature is an important factor in global warming studies and as a representative for climate change and radiation balance estimation in energy balance studies. Due to the special heat that each cover has on the ground. Vegetation land uses, barren lands, water resources, residential areas, absorb some of the sun's radiant energy and increase the temperature of the earth's surface. Finally, this heat is emitted from the surface of various coatings to the environment in the form of long wavelength radiation. If the surface temperature is calculated in different periods, the process of increasing or decreasing the surface temperature of different types of surface coverings can be modeled. MethodologyIn this study, to study the changes in land cover, MODIS images related to land cover from 2001 to 2019 were received. Surface cover product (MCD12Q1) Surface temperature product (MOD11) was prepared on a daily scale for both Terra and Aqua satellites to provide a variety of surface temperature indicators in the Google Earth engine system. In environmental studies, we often deal with observations that are not independent of each other and their interdependence with each other is due to the location and location of the observations in the study space. For this purpose, to reveal the effect of land cover on surface temperature components, global Moran correlation analysis tool was used and to analyze clusters and non-clusters, local Moran insulin index was used. In the last step, to evaluate the relationship between circadian surface temperature, daily temperature and night temperature After converting NDVI and LST raster maps to vector maps, Pearson correlation coefficient, regression relationship and significant value between variables in R programming environment were calculated.DiscussionBased on the land cover product of Modis 5 sensor, the predominant cover including shrubs, grasslands, agricultural lands, scattered vegetation and residential areas were identified between 2001 and 2019. The largest area of the region is scattered vegetation (50%) and secondarily grasslands (20%). During these 19 years, the cover of shrublands and the cover layer of scattered plants has an increasing trend and the cover of grasslands and arable lands has a decreasing trend. The surface temperature of this region has a spatial structure and is distributed in the form of clusters, so it has a spatial relationship with the natural features of the region. Spatial patterns of spatial data on surface temperature are divided into three categories: hot spots, cold spots, and clusters. Low-lying areas of the south and part of the east and west of the area, hot spots, high-altitude areas that include parts of the central areas in the south and north of the area, cold spots and cold spots margin, clusters (foothills) they give. On the 24-hour surface temperature scale, the land use layer of settlements and agricultural lands shows the most significant relationship between the types of land surface cover. In the daily temperature scale, the land use layers, grasslands and scattered vegetation have a decreasing trend and the use layer of shrubs and settlements has an increasing temperature. At night surface temperature scale, the trend of significant surface coatings in relation to the microclimatic element of surface temperature intensifies so that field cover, scattered vegetation and habitat layer have the highest correlation with increasing night surface temperature Show them selves. Therefore, in the study of spatial pattern of surface temperature, latitude and altitude are the most influential factors and in the study of the effects of land cover, the layer of settlements in three surface temperature parameters (minimum, maximum, average) of the highest temperature increase compared to others. Uses have been enjoyed. ConclusionLand use type and land use changes and vegetation have a significant effect on land surface temperature changes. In the northeastern region of the country, shrub cover, grasslands, arable lands, scattered vegetation cover and residential areas are the dominant cover of the region. During 19 years, the increase in the area of scattered vegetation and barren shrubs indicates negative changes in the ecosystem of the region. In such a way that the area of other classes such as arable lands and grasslands has been reduced and the area of these classes has been increased. The surface temperature of this region has a spatial structure and is distributed in the form of clusters in 3 clusters. Hot clusters, low-lying areas, cold clusters, high-altitude areas and inconveniences covered the foothills. Elevation factor, latitude are influential in the distribution of clusters. In studying the effects of land cover on the surface temperature of the land, during 19 years, the circadian temperature of the settlement layer has increased by about 1.12 degrees and the arable land layer by 0.41 degrees Celsius. On the daily temperature scale, the settlement layer has a temperature increase of about 1 degree. At night surface temperature scale, arable land cover, scattered vegetation cover and habitat layer recorded 6.2, 0.8 and 0.6 ° C temperature increase, respectively.
