سنجش میزان خودهمبستگی فضایی دمای سطح زمین با کاربری اراضی، نمونه مطالعه: شهر اصفهان (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
جزایر حرارتی شهری و ضریب گسیل سطحْ شاخص های مهمی در مطالعه مدل های تعادل انرژی در سطح زمین و بررسی فعل وانفعالات سطح زمین در مقیاس منطقه ای و جهانی هستند. در این مقاله، اثر جزیره گرمایی شهری با استفاده از داده های ماهواره لندست سنجنده OLI سال 2019 شهر اصفهان تجزیه و تحلیل شد. تصاویر انتخاب شده، طول موج 11٫30-10٫30 میکرومتر و تفکیک مکانی 100 متر و باند حرارتی 10 دارند. دمای سطح زمین (LST) و NDVI در گوگل ارث انجین برای تابستان سال 2019 محاسبه شد. هدف پژوهش حاضر ارزیابی خودهمبستگی فضایی جزایر حرارتی و ارتباط آن با کاربری اراضی شهر اصفهان است. برای استخراج کاربری اراضی، از روش طبقه بندی نظارت شده و الگوریتم حداکثر مشابهت استفاده شد. همچنین برای مشخص شدن الگوی پراکندگی، شاخص میانگین نزدیک ترین همسایگی به کار رفت. نتایج نشان داد که خوشه بندی در دمای سطح زمین با سطح اطمینان بیش از 99 درصد وجود داشته است. رابطه میان جزایر حرارتی با کاربری های ساخته شده و بایرْ مستقیم و افزایشی، و ارتباط آن با کاربری های پوشش گیاهی و آبْ معکوس است. مناطق 4، 5، 6 و 12 بیشترین میزان دما و مناطق 1، 2 و 3 کمترین میزان دما را داشته اند. همچنین نتایج حاصل از لکه های داغ و سرد نشان داد که لکه های سرد در مناطق مرکزی و لکه های داغ در مناطق جنوب شرقی قرار دارند.Measuring the Degree of Spatial Autocorrelation of Land Surface Temperature with Land Use (Isfahan City)
Extended abstract: Urban Heat Island (UHI) is an important factor for heat change and balance in global studies and is considered as a proxy for climate change. Studying this phenomenon and investigating its mechanism are very important in urban planning. During the last two decades, the great need for earth surface temperature information for environmental studies and land resource management activities has turned earth surface temperature remote sensing into one of the most important scientific issues (Sobrino et al., 2004). In a research, Hay et al. (2018) studied the effect of the topographical factor on LST in mountainous areas. The results of their research showed that there was a high correlation between topography parameters and surface temperature. For the area studied in this research, the relationship between LST and altitude was inversely linear. Tran et al. (2017) used Landsat 5,7,8 in order to determine the relationship between land cover change and land surface temperature of the inner city of Hanoi (plain and flat area). The results revealed that: a) LST depends on the non-linear method of LULC types; b) Foci analysis by using the Getis Ord Gi* statistic allows analysis of LST pattern change over time; c) UHI is affected from both the urban perspective and type of urban development; d) investigation of LST pattern prediction and UHI effect can be done by the proposed model by using nonlinear regression and simulated LULC change scenarios. In a research in a city of China, the harmful effects of land cover and land use changes on the Earth's surface temperature were investigated through vegetation indicators based on three image sensors of TM and ETM+. For this purpose, the researchers obtained the Temperature Vegetation Index (TVX) from the images. Their results showed that land use change is an important factor for increasing the Earth's surface temperature. It also showed high temperature in areas with scattered vegetation and low temperature in areas with dense vegetation. It has been used in the statistical period of 2013-2015 in the city of Isfahan as well. The results of this research showed that there was a sharp thermal gradient due to the presence of Urban Cold Islands (UCI) between the center and suburbs. The largest UCI was identified in Region 6 (Ahmadi et al., 2016). Keywords: Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), spatial statistics, Isfahan City Assumptions It seemed that the growth and development of the city had increased the number of heat islands. It seemed that the heat islands had spread from the surroundings towards the city center in the investigated time period. General purpose Measuring the degree of spatial autocorrelation between land surface temperature and land use Sub-goals: Survey of land use in the summer of 2019 Spatial survey of thermal penalty in the summer of 2019 Research method: In this research, 6 images obtained from Landsat 8 satellite were used Isfahan city in the summer of 2019. Landsat has two sensors; one is called Operational Land Imager (OLI) for Earth observation and the other is Thermal Infra-Red Sensor (TIRS) thermal observation. These two sensors form 11 bands together. The selected images had the wavelengths of 10.30-11.30 µm, spatial resolution of 100 m, and thermal band of 10. The pictures were taken in the warm season when we had the least amount of cloud cover. To prepare the data, atmospheric and geometric corrections on satellite data were used from the digital height data. Then, the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were calculated by using Google Earth Engine and radiometric corrections were implemented on the data. Results and conclusion: This research paid attention to changes in the spatial autocorrelation pattern of surface temperature and its relationship with land use. For this purpose, hot spot index and nearest neighbor average were used. ArcGIS was used to apply the methods. The results of the nearest neighborhood average index showed that Isfahan had some changes in the spatial correlation of ground surface temperature with a high confidence level and the dispersion was clustered at the 99% confidence level. Based on the patterns of hot spots in Regions 5 and 6, which were located in the south of the region, it played a significant role in the formation and creation of thermal islands with 99% hot clusters. There were areas of negative spatial autocorrelation (99% cold clustering areas) in the center of Isfahan city where Regions 1 and 3 existed due to the presence of water use (33 bridges) and thus, a significant part of Isfahan city lacked a statistically significant pattern. The results showed that the areas with vegetation and water use weakened the heat island effect and the areas, in which the built regions were expanded accelerated the heat island effect. In the planning and development of the city, more attention should be paid to greening the city of Amar. Spatial statistics studies can lead to a suitable and new model for the officials, politicians, and urban planners to use in the future. References - Clark, w.a.v & Hosking, p.l (1986). Statistical Methods for geographers, John Wiley, (32)2, 65-68. - Guo, A.Yang J,Sun W,Xiao X (2020). Impact of urban morphology and landscape characteristics on spatiotemporal heterogeneity of land surface temperature. 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Figures and Tables - Fig. 1: Map of the study area (Source: authors, 1400) - Fig. 2: Steps to examine the changes through Landsat satellite imagery - Fig. 4: Land use map of Isfahan City in 2019 (Source: writers, 2021) - Table 2: Areas of landuse per square kilometer in 2019 (Source: authors, 2021) - Table 3: Minimum, average, and maximum changes of ground surface temperature in land use (Source: authors, 2021) - Fig. 5: Spatial autocorrelation of surface temperature (Source: authors, 2021) - Fig. 6: Clustering of hot and cold spots (Source: authors, 2021) - Fig. 7: Charts of land use share in hot and cold spots (Source: authors, 2021)
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