Satellite remote sensing data and their interpretations for geothermal applications: A case study on the N gada District, central Flores, Indonesia

稔, 浦井; 洋文, 村岡; Nasution, Asnawir

doi:10.9795/bullgsj.53.99

Cited by 9 publications

(1 citation statement)

References 4 publications

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“…Technological advancement in remote sensing (satellite sensor: optical or thermal) nowadays makes it feasible to map potential geothermal heat anomalies at a large scale effectively by utilizing various features aboard the satellites that can assist in determining geothermal activity such as land surface temperature, land feature classification, lineaments mapping and drainage. (Hewson et al 2020;Hariyanto and Robawa 2016;Srivastava et al 2010;Urai et al 2002;Bouazouli et al 2019;Abubakar et al 2019). The relatively save time consumption, cheap and high observation accuracy makes it useful as the first phase in exploration to supply data and information for farther studies.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Satellite Imagery and In-Situ Data for Identifying Geothermal Potential Sites: Jaboi, Indonesia

Isa

Cesarian

Abir

et al. 2020

Int. J. Renew. Energy Dev.

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Remote sensing makes it possible to map potential geothermal site for a large area effectively using thermal infrared. The purpose of the present research is to overlay ground temperature, resistivity and satellite retrieved temperature in identifying geothermal potential site in Jaboi, Sabang-Indonesia. The data of acquisition of the DEM imagery was January 3rd, 2009 and the Landsat 8 imagery is July 18th, 2017. The satellite data were applied to extract the land surface temperature and land classification across. Two supporting data in situ were used to validate the results from remote sensing. First dataset was ground temperature measurements with total 114 points and second dataset was vertical electrical sounding (VES) with total of 51 points. Satellite, VES and ground temperature data were processed and analysed using the Envi 5.3, PCI Geomatica 2016 and ArcMap 10.4. The results from each data were integrated to produce a map shows geothermal potential. Its integration produced four areas which were considered to have high geothermal potential. However, these areas vary in term of the clustering of the features of interest, for example lineament and drainage density of the area, high temperature in the surface area, fault existence and low resistivity subsurface. All the features must take into consideration to rank potential area which has higher potential. Finally, a map of geothermal potential across were successfully created as an insight for future reference. ©2020. CBIORE-IJRED. All rights reserved

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Section: Introductionmentioning

confidence: 99%