شناسایی مناطق مستعد زمین گرمایی سطحی با ترکیب دمای سطح و فلوهای انرژی حاصل از روش سبال

آشکارسازی و شناسایی مناطق مستعد زمین گرمایی سطحی و کم عمق، با استفاده از داده های سنجش از دور یکی از روش های نوین در بسیاری از کاربردها می باشد. هدف این مطالعه شناسایی مناطق مستعد زمین گرمایی سطحی با ترکیب دمای سطح و فلوهای انرژی حاصل از روش سبال با استفاده از داده های لندست 8 در شمال غرب کشور مورد ارزیابی و تجزیه و تحلیل قرار گرفته است. برای این منظور و به دلیل حداقل بودن اثر تشعشعات خورشیدی، دو فریم از داده های لندست 8 به تاریخ 13 اکتبر 2016 مورداستفاده قرار گرفت. سپس با استفاده از دو الگوریتم تک باندی محاسبه دما، معادله انتقال تشعشع حرارتی و الگوریتم جیمز سوبرینو (scjm s)، نقشه دمای مشاهداتی سطح زمین مشخص گردید. همچنین با استفاده از الگوریتم توازن انرژی در سطح زمین (سبال) مقدار تشعشعات خالص دریافتی توسط سطح زمین ، انرژی خالص هدایت شده به زمین و مقدار تابش جذب شده خورشیدی توسط سطح محاسبه گردید. برای اولین بار، با ترکیب این لایه های اطلاعاتی با دمای مشاهداتی سطح، مناطق مستعد انرژی زمین گرمایی شناسایی و تعیین گردیدند. نتایج نشان داد که مناطقی مابین شهرهای مرند و تسوج و همچنین مابین شهرهای خوی و قطور مناطق مستعد زمین گرمایی سطحی می باشد که وجود آبگرم طبیعی در منطقه قطور احتمال وجود منابع زمین گرمایی را افزایش داده و موءید این مطلب می باشد. همچنین، نتایج مشابه در مناطقی در جنوب شهر ارومیه و در غرب شهر اشنویه حاصل گردید. این مناطق حاصل، حداکثر فاصله ای که از محل مصرف انرژی (شهرهای ارومیه، خوی، مرند، تسوج، شرفخانه و اشنویه) دارند، برابر 30 کیلومتر می باشد که از لحاظ اقتصادی قابل توجیه بوده و می توانند بخش عظیمی از انرژی مصرفی را در صنعت و شهرها تامین نموده و محیط زیست سالم و پاک را به ارمغان بیاورد.

Shadow Geothermal Energy Detection using Integrating of Temperature Anomalies and SEBAL Algorithm

With the increase in world population, industrialization and improvement in the standard of living, there has been a continuous increase in consumption of energy. In the recent years, a new resource of energy, gashydrates, is drawing worldwide attention. Detection and identification of suitable areas of shallow geothermal energy, using remote sensing data is one of the new methods in many applications. In areas of anomalously high heat flow, geothermal systems transfer heat to the Earth rsquo;s surface often forming surface expression such as hot spring, heated ground, and associated mineral deposits. Geothermal systems are increasingly important as sources of renewable energy, or as natural wonders of protected status attracting tourists, and their study is relevant to monitoring deeper magmatic processes. Thermal infrared (TIR) remote sensing provides a unique tool for mapping the surface expressions of geothermal activity as applied to the exploration for new geothermal power resources and long term monitoring studies. Airborne and space borne TIR data supports longterm monitoring of geothermal systems by providing a rapid and repeatable method of inventorying surface geothermal features. In addition, methods for relating the temperatures of surface geothermal phenomena to estimates of nearsurface heat loss provide important inputs to the monitoring of geothermal activity and as applied to geothermal resource assessment and modeling. A geothermal resource can be simply defined as a reservoir inside the Earth from which heat can be extracted economically (cost wise less expensive than or comparable with other conventional sources of energy such as hydroelectric power or fossil fuels) and utilized for generating electric power or any other suitable industrial, agricultural or domestic application in the near future. Geothermal resources vary widely from one location to another, depending on the temperature and depth of the resource, the rock chemistry and the abundance of groundwater. Utilization of geothermal resources can broadly be classified into electric power generation and nonelectric use. The type of the geothermal resource determines the method of its utilization. This research is based on applications of remote sensing as a decision support system that focused on the exploration of geothermal energy and environmental management. The aim of this study is to identify suitable areas for Shadow geothermal energy detection by integrating of land surface temperature (LST) anomalies and the energy flows of surface energy balance algorithms for land (SEBAL) algorithm using data LDCM data, , has been evaluated and analyzed in the North West of Iran. To this end, and because of at least the effect of solar radiation, two examined the scenes of LDCM data was used for dates October 13, 2016. Then, using two singleband algorithms (Radiative Transfer Equation (RTE) and SCJM S) to calculate the LST and the LST anomaly maps of were identified. In addition, using the SEBAL Algorithm was calculating the amount of net radiation received by the Earth's surface (Rn), the amount of heat flow between the different layers of soil (G) and the amount of radiation absorbed by the solar surface (Rsolar). By assessing and combining this information layers with the LST anomaly maps the shadow geothermal prone areas were identified and determined. The results showed that the areas between the cities of Marand and Tasuj as well as between Gator and Khoy cities prone shadow geothermal areas, the existence of large natural spa in the region, the possibility of geothermal resources increases and this is confirmed . Also, similar results were obtained in areas south of the city of Urmia and west of Oshnavieh. These obtained areas have the maximum distance that the location of energy consumption (in Urmia, Khoy, Marand, Tasuj, Sharafkhaneh and Oshnavieh) equal to 30 km, which is economically justified and it can provide a large part of the clean energy used in industry and cities and brings a healthy environment.