Thermal Remote Sensing at Leyte Geothermal Production Field using Mono-window Algorithms

Authors: Serafin Farley MENESES
Keywords: thermal remote sensing, gis, land surface temperature
Conference: World Geothermal Congress Session: Exploration
Year: 2015 Language: English
Abstract: Other countries are now using thermal remote sensing as a tool for geothermal energy resource exploration and management. Researchers from Indonesia uses LandSat data to map out potential geothermal sites at Patuha, West Java, Indonesia (Siahaan, Soebandrio and Wikantika 2011) while researchers from Nevada, USA uses ASTER thermal infrared images to detect and monitor geothermal anomalies within their study area (Coolbaugh, et al. 2007). In EDC, thermal remote sensing is introduced as a possible tool for mapping anomalous areas for geothermal exploration by generating land surface temperature (LST) maps using remote sensing datasets. Single channel/Mono-window algorithms were used to generate the LST maps. Two LST maps were derived for Leyte by using 1996- and 2010-acquired LandSat 5 images that are almost cloud free. Agro-meteorological data from the Philippine weather agency were also integrated in the LST map derivation to take into account the climate, weather, and ground conditions during the time the images were captured. To validate the results that were derived from remote sensing, in-situ ground temperature measurements were conducted using a thermocouple to measure kinematic temperature. Twenty five (25) locations were used to calibrate the data and it was found out that the satellite-derived temperature values gave good correlation with the ground measurements, with variances ranging from 1.52 deg C to 3.00 degrees C. To complement thermal mapping for future activities, EDC will also look into the possibility of using GIS and geostatistics to use the derived LST maps to determine possible drilling targets by combining the information from the thermal maps with other datasets like structural maps, geophysical maps, and digital elevation models.
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