Geothermal Anomaly Mapping Using Landsat ETM+ Data in Ilan Plain, Northeastern Taiwan

Abstract

Abstract-Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field. Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery. This study aims to apply and integrate thermal infrared (TIR) remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM?) imagery is used to retrieve the land surface temperature (LST) in Ilan plain. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations. The correlation coefficient of linear regression between air temperature and LST retrieval is 0.76. The MODIS LST product is used for the cross validation of Landsat derived LSTs. Furthermore, Landsat ETM? multi-temporal brightness temperature imagery for the verification of the LST anomaly results were performed. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings. It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping and quantifying surface features to facilitate the exploration and assessment of geothermal resources in Taiwan.

Landsat Time-series Images-based Urban Heat Island Analysis: The Effects of Changes in Vegetation and Built-up Land on Land Surface Temperature in Summer in the Hanoi Metropolitan Area, Vietnam

“…Data from Table 2 illustrates the minimum and maximum differences in values between estimated LSTs and the meteorological air temperatures (MATs) were 1.1-4.2 o C, 0.9-4.0 o C and 0.6-4.0 o C at the three time points, respectively. These positive differences are similar to those reported in previous studies of Chan et al (2018) and Rhee and Im (2014) that the day LST is higher than air temperature. The two main metrics, root mean square error (RMSE) and mean absolute error (MAE), were determined with the values of 2.3 o C and 2.0 o C on 30 September 1996, 2.6 o C and 2.6 o C on 24 May 2007, and 2.2 o C and 1.9 o C on 01 June 2016.…”

Identification of Geothermal Reservoirs in South Cameroon from a Combined Landsat 8 and AMT Data

“…To estimate the average depth of the Moho on the cardboard at 45 km and under the Kribi-Campo domain at 28 km, [8] performs a joint inversion of Rayleigh group velocities and receiver functions by analyzing seismic data from a temporary network installed in Cameroon between 2005 and 2007. In addition to these indicators, the strong predominance of faults and lineaments is conducive to the development of geothermal sites [9]. The vast network of faults identified in the South Cameroon region through the geological map (Figure 2) of [10] and highlighted by [11] with the directions (N-S, E-W, SW-NE, and SSW-NNE) constitutes a major asset in the prospecting of geothermal sources.…”

Exploring and monitoring geothermal and volcanic activity using Satellite Thermal Infrared data in TVG, Taiwan

“…The temperatures may rise rapidly as the time approaches toward the noon and the highest temperatures are present at the noon. The scale-mismatch effect between the 30 m and 1 km spatial resolutions is also considerably responsible for discrepancies (Chan et al 2018). (2002 -2016) for Monitoring TVG Landsat 7 dataset is not sufficient for long term monitoring of the LST in TVG due to the satellite's low temporal resolution (revisit in 16 days).…”