Annisha Bunga Fathya
Institut Teknologi Sumatera

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ANALISIS ANOMALI LAND SURFACE TEMPERATURE MENGGUNAKAN CITRA SATELIT LANDSAT 8 UNTUK IDENTIFIKASI PROSPEK PANAS BUMI GUNUNG SIRUNG DI KABUPATEN ALOR, PROVINSI NUSA TENGGARA TIMUR: ANALYSIS OF ANOMALY LAND SURFACE TEMPERATURE USING LANDSAT 8 SATELLITE IMAGERY TO IDENTIFY GEOTHERMAL PROSPECT OF MOUNT SIRUNG IN ALOR REGENCY, EAST NUSA TENGGARA PROVINCE Annisha Bunga Fathya; Husin Nugraha; Alvira Gusti Ranti; Rina Wahyuningsih
Buletin Sumber Daya Geologi Vol. 18 No. 1 (2023): Buletin Sumber Daya Geologi
Publisher : Pusat Sumber Daya Mineral Batubara dan Panas Bumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47599/bsdg.v18i1.367

Abstract

High anomaly analysis of Land Surface Temperature (LST) on Landsat 8 imagery is a remote sensing method to detect the presence of geothermal prospects. This analysis is carried out through the presence of ground surface temperature anomalies. This study aims to identify the indication of Mount Sirung geothermal prospect in Alor Regency, East Nusa Tenggara Province through high LST anomaly analysis which is useful for determining more specific areas for field survey locations. The research method used is LST with a single-channel algorithm using Landsat 8 imagery and determination of high temperature indications based on LST anomalies with natural breaks classification, percentiles, and classical statistics. The Thermal Infrared Sensor (TIRS) band of Landsat 8 imagery could be a cheap and fast alternative for estimating land surface temperature analysis using the LST anomaly determination method which is useful for determining the boundaries of geothermal prospect indication areas prior to field surveys. This is evidenced by the suitability between the LST high anomaly area and the geothermal prospect area resulting from a field survey. The classification method for determining anomaly values ​​using the natural break and percentile approaches is better than the Classic Statistics approach (mean + 2 Standard Deviation) because the natural breaks and percentile approaches are more robust to the shape of the data distribution, therefore the resulting high anomaly areas becomes detailed and specific.