کاربرد الگوریتم پنجره مجزا در شناسایی جزایر حرارتی شهرستان یزد

در سال‌های اخیر، فعالیت‌های انسانی مانند تغییر کاربری، پوشش اراضی و توسعه مناطق انسان‌ساخت موجب افزایش دمای سطح زمین و پیدایش جزایر حرارتی شده است. در پژوهش حاضر تغییرات دمای سطح زمین در پوشش اراضی شهرستان یزد با استفاده از داده‌های ماهواره لندست 8 بررسی شد. نقشه کاربری اراضی منطقه مطالعاتی با استفاده از روش ماشین‌بردار پشتیبان در پنج کلاس انسان‌ساخت، پوشش گیاهی، پیکره آبی، اراضی بایر و رخنمون سنگی، تهیه و برای استخراج نقشه دمای سطح زمین از روش پنجره مجزا استفاده شد. به‌منظور شناسایی الگوی حاکم بر دمای سطح زمین از شاخص موران جهانی استفاده و لکه‌های داغ با به‌کارگیری آماره گتیس -ارد جی شناسایی شدند. بر اساس نتایج، اراضی بایر بیش‌ترین و پیکره آبی کم‌ترین مساحت را به‌ترتیب برابر با 129360/96و 160/56 هکتار به خود اختصاص داده‌اند. میانگین دمای سطح زمین برابر با 50/83 درجه سانتی‌گراد به‌دست آمد و پیکره آبی، کم‌ترین و اراضی بایر بیش‌ترین میانگین دمای سطح زمین را به‌ترتیب برابر با 36/91 و 52/13 درجه سانتی‌گراد به خود اختصاص داده بودند. داده‌های دمای زمین شهر یزد با مقدار شاخص موران 0/92 دارای خودهمبستگی فضایی و الگوی خوشه‌ای بودند. در این پژوهش، مساحت لکه‌های داغ و سرد به‌ترتیب برابر با 46539/18 و 113553/81 هکتار به‌دست آمد. اراضی بایر، تشکیل جزایر حرارتی داغ و پوشش گیاهی و پیکره آبی تشکیل جزایر سرد را داده‌ و جزیره حرارتی داغ با گستردگی بالایی اطراف محدوده شهری را احاطه کرده بود. نتایج پژوهش نشان داد که کیفیت کلاس پوشش گیاهی ضعیف و میانگین دمای آن برابر با 45/61 درجه سانتی‌گراد است. بر اساس نتایج حاصل از رگرسیون خطی، بین شاخص گیاهی تعدیل‌کننده اثر خاک (savi) با دمای سطح زمین همبستگی منفی و معنی‌دار (0/51 -=r) در سطح احتمال یک درصد به‌دست آمد.

application of split-window algorithm to study urban heat island in yazd county

introduction lst (lst) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. human activities such as land use changes and the development of urban areas led to an increase in the lst and the appearance of thermal islands. the main source of climate data such as temperature are synoptic stations, however, it is impossible and timeconsuming to use traditional methods to estimate the lst for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth’s surface. at present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting lst and land use maps. spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. the aim of this research is to extract lst and land use map of yazd county using a remote sensing technique. in this study, the spatial autocorrelation of lst in yazd city and the identification of hot thermal clusters have been investigated using the global moran statistic and the getisord gi statistic. materials and methodsin this research, landsat 8 satellite’s multispectral and thermal images have been used to extract the land use and lst in the study area, after performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (builtup, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. in the next step, lst was extracted by the split window method. the relationship between lst and soil adjusted vegetation index (savi) was investigated using regression analysis. in order to identify the spatial pattern of the lst, the global moran index was used and hot spots were identified by getisord gi statistics. results and discussionour findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. the average lst was 50.83°c. the result showed that the type of land use had an effect on lst, the water bodies had the lowest, and barren lands had the highest mean lst, equal to 36.91 and 52.13 °c, respectively. vegetation is one of the factors that regulate the lst, areas without vegetation have a maximum lst and areas with high density vegetation