تاثیر الگوهای سیمای سرزمین بر تغییرات دمای شهری در همدان

شهرسازی و مناطق شهری اثر قابل توجّهی بر آب وهوای محلّی و جهانی دارند. یکی از مهم ترین این اثرات، تغییرات دمای سطح شهری است که شامل جزایر و چاله های حرارتی در سطح شهر می شود. نوشتار پیش رو با هدف بررسی تغییر الگوهای سیمای سرزمین و کارایی سنجه های سیمای سرزمین در تحلیل تغییرات دمای سطح شهری با استفاده از تصویر ماهواره ای لندست 8 در محدوده شهرستان همدان صورت گرفته است. به این منظور پس از محاسبه دمای سطح زمین، جزایر و چاله های حرارتی مشخّص شد، کاربری های موجود در این مناطق شناسایی شده و درنهایت سنجه های سیمای سرزمین در سطح کلاس و سیمای سرزمین با نرم افزار فراگستات استخراج شد. نتایج این مطالعه نشان داد که بالاترین دما در محدوده مورد مطالعه مربوط به پهرو خاک و فاقد پوشش گیاهی بود؛ به عبارتی مولّفه اصلی چاله های حرارتی، لکّه کشاورزی است درحالی که، لکّه خاک عضو اصلی تشکیل دهنده جزایر حرارتی است. رابطه دمای سطح زمین با سنجه ها در سطح سیمای سرزمین نیز بررسی شد، در جزایر بین دما و تراکم لکه، همبستگی منفی و در چاله حرارتی؛ برعکس جزیره حرارتی، دما با سنجه تراکم لکه رابطه مثبت و معنی دار و با سنجه شاخص تجمّع همبستگی منفی داشت. هرچه پیوستگی بین کاربری و الگوهای سیمای سرزمین بیشتر باشد دما پایین تر و هرچه قطعه قطعه شدگی افزایش یابد، روی دما تاثیر افزایشی دارد. نتایج بیانگر آن است که دمای سطح زمین در شهرستان همدان تنها تحت تاثیر ترکیب کاربری و پوشش سطح زمین نیست؛ بلکه پیکره بندی فضایی و ساختار سیمای سرزمین نیز در آن تاثیرگذار است.

The Effect of Landscape Pattern on Urban Temperature Changes in Hamadan

Urbanization and urbanized areas have a significant impact on local and global climate. One of the most important its effects is surface urban thermal variations (SUTV), including surface urban heat islands (SUHI) and surface urban heat sinks (SUHS). The purpose of this study is to investigate the impacts of landscape pattern changes and the efficiency of landscape metrics in analyzing surface urban thermal variations in Hamadan using Landsat 8 satellite images. Having calculated land surface temperature (LST), the SUHI and SUHS regions were identified, the existing land uses in these areas were classified and finally landscape metrics at class and landscape levels were extracted using FRAGSTATS software. The results showed that the highest temperature in the study area was seen in the areas with no vegetation cover, in other words the main component of SUHS is agriculture patch, while soil patch is the main constituent of SUHI. The relationships between LST and landscape metrics were also examined. LST and patch density (PD) were negatively correlated. In SUHS, in contrast to island heat areas, LST had a negative correlation with AI metrics and had a positive and significant relationship with PD metrics. The higher the consistency between land use and landscape patterns, the lower the temperature and the higher the fragmentation, the higher the temperature effect. The results indicated that land surface temperature in Hamadan is not affected just by land use composition and land cover, LST in Hamadan is not only affected by the combination of land cover, but also the spatial configuration and structure of the landscape is influenced.Extended Abstract1Introduction Urbanization and urban areas have a significant impact on local and global climate. One of the most significant impacts is the urban surface heat changes, which include Surface Urban Heat Islands and Surface Urban Heat Sinks. Analyzing heat islands and heat sinks, and understanding the relationship between them and urban landscape patterns can provide valuable information for designing effective mechanisms to reduce heat islands in the urban scale.In order to analyze the landscape pattern, in addition to the shape, size and type of patches, the proximity of the patches and the distribution of the landscape patterns should also be examined. Therefore, understanding the spatial distribution of landscape structure in complex patchwork mosaics has become one of the major issues in landscape pattern studies. The metrics are used as quantitative indicators for measuring and describing landscape patterns.2Materials and Methods For land surface temperature analysis, the advantage of satellite temperature data is advantageous due to the limited number of synoptic stations in the city and the high cost of collecting terrestrial data. It is possible to identify urban microclimate details with the help of satellite images obtained from surface heating patterns. The purpose of this study is to investigate the effect of changes in urban landscape patterns on urban surface temperature in Hamadan county using Landsat 8 satellite images.The Hamadan County is located in the middle part of Hamadan province with an area of ​​2831 km2. It is naturally located in a mountainous area and has a variable climate. In this study, the Landscape of Hamadan was divided into eight categories: water, impermeable surfaces, soil, agriculture, rangeland, dry farms, saline soils and other. The points were sorted visually.The Landsat 8 image thermal band and ENVI software were used to calculate the surface temperature. To identify the urban temperature changes, the definition of the urban heat islands proposed by Hu and Brunsell (2013) was applied to identify the heat islands and heat sinks based on the surface temperature changes throughout the study area.To analyze the variation of landscape patterns within the area, surface temperature variations were calculated based on the classification results, a set of landscape metrics (including composition and configuration) within the heat islands and heat sinks. Six classes and three landscapes were considered based on landscapes measurements using FRAGSTATS software.Percentage of landscape, Largest Patch Index, Patch density, Aggregation Index, Fractal dimension index and Mean NearestNeighbor were the considered Landscape metrics at the class level; and the percentage of land cover, the largest patch index and the aggregation index were considered at the landscape level. The Class level metrics were calculated without sampling.Spearman correlation was used to examine the relationship between land surface temperature and surface spatial metrics in heat islands and sinks in the study area.3Results and Discussion In the whole study area, mean surface temperature was 39.71 ° C, standard deviation 4.4, minimum temperature was 19.56 and maximum was 50.87 ° C. The first category of temperature represents the coolest zones, or heat sinks, and includes mountainous areas and agricultural lands with dense vegetation. The second category covers most of the central part of the county with thin vegetation, and the third one, or thermal islands, is most prone to agricultural lands and has much lower vegetation coverage, or they are without vegetation.The maximum land surface temperature is in the bare land and the surface temperature is drastically lower in the relatively dense vegetation. In the heat islands, there was a negative correlation between land surface temperature and patch density with a correlation coefficient of (0.395); and a positive correlation with aggregation index and largest patch index with correlation coefficient of 0.221, 0.226In the heat sinks, in contrast to the heat islands, the aggregation index and the largest patch index had a negative correlation with the patch density index.At the classlevel, the bare soil occupies a large proportion of the urban thermal islands, and the lowest percentage is for the agricultural class. While within the heat sinks of the city, the agricultural lands accounted for the largest percentage. This indicates that the composition of the landscape has a major influence on the surface temperature.At the landscape level, where the whole area was considered to be a unified landscape, there was a significant negative correlation between the aggregation index and the largest patch index with surface temperature in the urban heat sinks.4Conclusion While dense urban zones were expected to have maximum temperatures, in this study the impermeable surfaces did not have high temperatures. Rather, the vegetation had the main role so that areas with high cover density had low surface temperature, and the areas with low vegetation cover and bare soils showed high surface temperatures. Therefore the vegetation was the most effective factor in the surface temperature changes in the study area.The results relatively revealed that by modifying the composition and configuration of the landscape at the class and landscape levels, the effects of urban heat islands could be mitigated or the effects of urban heat sinks increased.