بررسي ارتباط كاربري‌هاي مختلف با دماي سطح زمين مبتني بر تحليل خودهمبستگي فضايي (موران) با استفاده از داده‌هاي تصاوير ماهواره‌اي لندست (مطالعه موردي: شهرستان كوثر)

Investigating the Relationship between Different Uses with Earth's Surface Temperature Based on Spatial Autocorrelation Analysis Using Landsat Satellite Image Data (Case Study: Kosar County

ﺣﺮارت ﺳﻄﺢ زﻣﯿﻦ ﺷﺎﺧﺺ ﻣﻬﻤﯽ در ﻣﻄﺎﻟﻌﻪ ﻣﺪل ﻫﺎي ﺗﻌﺎدل اﻧﺮژي در ﺳﻄﺢ زﻣﯿﻦ در ﻣﻘﯿﺎس ﻣﻨﻄﻘﻪ اي و ﺟﻬﺎﻧﯽ اﺳﺖ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻣﺤﺪودﯾﺖ اﯾﺴﺘﮕﺎه ﻫﺎي ﻫﻮاﺷﻨﺎﺳﯽ، ﺳﻨﺠﺶ از دور ﻣﯽ ﺗﻮاﻧﺪ ﺟﺎﯾﮕﺰﯾﻦ ﻣﻨﺎﺳﺒﯽ ﺑﺮاي ﺑﺮآورد ﺣﺮارت ﺳﻄﺢ زﻣﯿﻦ ﺑﺎﺷﺪ. در ﭘﮋوﻫﺶ ﺣﺎﺿﺮ ﺑﻪ ارﺗﺒﺎط ﮐﺎرﺑﺮي ﻫﺎي ﻣﺨﺘﻠﻒ ﺑﺎ دﻣﺎي ﺳﻄﺢ زﻣﯿﻦ ﺷﻬﺮﺳﺘﺎن ﮐﻮﺛﺮ ﺧﻮدﻫﻤﺒﺴﺘﮕﯽ ﻓﻀﺎﯾﯽ ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺ ﻣﻮران ﭘﺮداﺧﺘﻪ ﺷﺪه اﺳﺖ. ﺑﻪ اﯾﻦ ﻣﻨﻈﻮر از ﺗﺼﺎوﯾﺮ ﻣﺎﻫﻮاره اي ﻟﻨﺪﺳﺖ 8 )OLI( و ﻟﻨﺪﺳﺖ 5 )TM( اﺳﺘﻔﺎده ﮔﺮدﯾﺪ. ﺗﺤﻘﯿﻖ ﺣﺎﺿﺮ در ﭼﻬﺎر ﻣﺮﺣﻠﻪ اﺻﻠﯽ اﻧﺠﺎم ﮔﺮﻓﺖ: اﻟﻒ( ﺗﻬﯿﻪ ﺗﺼﺎوﯾﺮ ﺳﺎل 1987 و 2015، ب( اﻧﺠﺎم ﭘﯿﺶ-ﭘﺮدازش ﻫﺎي ﻻزم، ج( ﺗﻬﯿﻪ ﻧﻘﺸﻪ ﻃﺒﻘﻪ ﺑﻨﺪي ﺑﺎ اﺳﺘﻔﺎده از روش ﺷﯽ ﮔﺮا و د( اﺳﺘﺨﺮاج دﻣﺎي ﺳﻄﺢ زﻣﯿﻦ ﺑﺎ اﻟﮕﻮرﯾﺘﻢ ﭘﻨﺠﺮه ﻣﺠﺰا. ﻧﺘﺎﯾﺞ ﺑﻪ دﺳﺖ آﻣﺪه ﻧﺸﺎن داد ﮐﻪ ﮐﺎرﺑﺮي ﮐﺸﺎورزي دﯾﻢ و ﻣﺮﺗﻊ در ﺳﺎل 1987 و 2015 داراي دﻣﺎي ﺑﺎﻻﺗﺮي ﻧﺴﺒﺖ ﺑﻪ ﺑﻘﯿﻪ ﮐﺎرﺑﺮي ﻫﺎ ﻣﯽ ﺑﺎﺷﻨﺪ ﮐﻪ دﻟﯿﻞ اﯾﻦ اﻣﺮ ﺧﺸﮏ ﺑﻮدن ﻣﺤﺼﻮﻻت ﮐﺸﺎورزي در ﺑﺎزه زﻣﺎﻧﯽ اﻧﺘﺨﺎب ﺷﺪه اﺳﺖ )ﻣﺎه ژوﺋﻦ( ﮐﻪ ﺑﺎﻋﺚ ﺑﺎﻻ رﻓﺘﻦ دﻣﺎي اﯾﻦ ﻣﺤﺼﻮﻻت ﺷﺪه اﺳﺖ. در ﻣﻘﺎﺑﻞ ﮐﺎرﺑﺮي ﻣﻨﺎﻃﻖ آﺑﯽ ﺑﻪ ﺧﺎﻃﺮ ﻇﺮﻓﯿﺖ ﮔﺮﻣﺎﯾﯽ ﺑﺎﻻي ﺧﻮد در اﯾﻦ ﺳﺎل ﻫﺎ ﺣﺪاﻗﻞ ﻣﯿﺎﻧﮕﯿﻦ دﻣﺎ را ﺑﻪ ﺧﻮد اﺧﺘﺼﺎص داده اﻧﺪ. همچنين با استفاده از شاخص تحليل لكه‌هاي داغ (Hotspot) خوشه‌هاي سرد و گرم جزاير حرارتي كوثر استخراج گرديد. در نهايت تحليل خودهمبستگي فضايي با شاخص‌هاي موران جهاني نشان داد كه دماي سطح زمين شهرستان كوثر داراي ساختار فضايي بوده و به شكل خوشه‌اي توزيع شده‌است.

Land surface temperature is an important indicator in the study of ground energy balance models on a regional and global scale. Due to the limitations of meteorological stations, remote sensing can be a good alternative to estimating surface temperature. In the present study, the relationship between different land uses and ground surface temperature in Kosar city and spatial autocorrelation has been investigated using Moran index. Landsat 8 (OLI) and Landsat 5 (TM) satellite images were used for this purpose. The present study was carried out in four main stages: a) preparing images for 1987 and 2015, b) making the necessary predictions, c) preparing a classification map using the object-oriented method, and d) extracting the earth's surface temperature with Separate window algorithm. The results showed that dryland and rangeland agricultural uses in 1987 and 2015 have higher temperatures than other uses, due to the dryness of selected agricultural products in the selected period (June), which caused the temperature to rise. Is. In contrast, the use of water areas due to their high heat capacity in these years have the minimum average temperature. In addition, hot and cold clusters of Kosar thermal islands were extracted using the hot spot analysis index (Hotspot). Finally, the analysis of spatial autocorrelation with global Moran indices showed that the surface temperature of Kosar city has a spatial structure and is distributed in the form of clusters.