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Title

Determining the best algorithm to calculate land surface temperature with the aim of identifying geothermal areas-Case study: MeshkinShahr County

Pages

  79-98

Abstract

 Introduction: Geothermal Energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development. However, detecting and exploring sources of this energy using modern and low cost methods: – as a replacement for land surveying methods:-can help planners and authorities working in the field of energy. In this regard, Thermal remote sensing with a vast coverage of the earth’ s surface, and the possibilityof calculating Land surface temperature using satellite imagery plays an important role as a new economic tool. Mapping Land surface temperature is a key point in achieving geothermal anomalies and different algorithms play an important role in Land surface temperature estimation. Therefore, identifying potential sources of Geothermal Energyusingremotely sensed thermal data is a challenging and yet interesting subject. Materials and methods: : The present study takes advantage of images received from OLI and TIRS sensors (Landsat 8) to estimate Land surface temperature, analyze thermal anomalies, and identify areas with potential geothermal resources in Meshkinshahr. The images were retrieved fromUSGSin Geo TIFF format. Envi 5. 3, eCognition 9. 1, MATLAB and ArcMap 10. 4. 1 were used to prepare, process and analyze the images. Moreover, meteorological data received fromMeshkinshahr station was collected from the General Department and Meteorological Center of Ardabil Provincewith the aim of identifying the optimal algorithm for calculation ofLand surface temperature. Data wascollected for a one-day period (31/08/2017), i. e. the same day Landsat 8 passed over the areaunder study. Results and discussion: The present study sought to identify areas with potential geothermal resources using Thermal remote sensing and a combination of surface temperature and Thermal Anomaly models. In order to calculate Thermal Anomaly, an observational thermal image is required, which is in fact the same Land surface temperature calculated using Split Window and Mono Window algorithmsfor the image received from the satellite thermal band at the moment of collecting images. It should be noted that the Land surface temperature calculated with these algorithms was evaluated using statistical data recorded in the temperature monitoring station. Results indicated higher accuracy of Split Window algorithm (3 ° C difference). Since, temperature obtained from this algorithm was more consistent with the actual temperature, its results were used as the observational thermal image. A thermal model was also defined to model factors responsible for heat variation from one pixel to another one. These two images were calculated and subtracted to reach the Thermal Anomaly image. In order to identify thermal anomalies caused by undergroundfactors heating the earthsurface, other factors responsible for increasing/decreasinglandsurfacetemperature should be normalized in the image. Thus, the effect of parameters such as solar energy, environmental degradation and evaporation on Land surface temperature obtained from split window algorithm was investigated and finally, areas with heat anomalies and evidences indicating the presence of geothermal resources around themwere selected as areas with potential geothermal resources. Results indicate that inthe area surroundingSabalanmountains, two regions with 5. 5 and 10. 05 hectares in the northern and northeastern parts of Moyelvillage, a1. 4 hectares area in the southwestern part of Qutursouli Spa, and the southern part of the Qinrjah Spa with an area of 1. 1 hectare had potentialgeothermal resources and a high potential for exploration of geothermal resources. Conclusion: The presence of hot springs, a geothermal power plant and other evidences shows that Ardabil Province and especially Meshkinshahr city has the potential for Geothermal Energy production as one of the major sources of new and environmentally friendly energies. However, no effective studies have been performed to identify these resources using modern and low-cost methods: including Thermal remote sensing. Therefore, the present study for the first time took advantage ofGIS and remote sensingto identify areas appropriate for Geothermal Energy extraction inMeshkinshahr city and concluded that remote sensing studies on Landsat 8 satellite images have a high efficiency for identifying areas with potential geothermal resources. Thus, areas identified in the present study have a strong spatial correlation with the geothermal evidences founded in the region.

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    APA: Copy

    Parhizcar isalu, Reza, VALIZADEH KAMRAN, KHALIL, & Faizizadeh, Bakhtiar. (2020). Determining the best algorithm to calculate land surface temperature with the aim of identifying geothermal areas-Case study: MeshkinShahr County. GEOGRAPHICAL DATA, 29(114 ), 79-98. SID. https://sid.ir/paper/951959/en

    Vancouver: Copy

    Parhizcar isalu Reza, VALIZADEH KAMRAN KHALIL, Faizizadeh Bakhtiar. Determining the best algorithm to calculate land surface temperature with the aim of identifying geothermal areas-Case study: MeshkinShahr County. GEOGRAPHICAL DATA[Internet]. 2020;29(114 ):79-98. Available from: https://sid.ir/paper/951959/en

    IEEE: Copy

    Reza Parhizcar isalu, KHALIL VALIZADEH KAMRAN, and Bakhtiar Faizizadeh, “Determining the best algorithm to calculate land surface temperature with the aim of identifying geothermal areas-Case study: MeshkinShahr County,” GEOGRAPHICAL DATA, vol. 29, no. 114 , pp. 79–98, 2020, [Online]. Available: https://sid.ir/paper/951959/en

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