Geothermal potential area analysis using Landsat 8 OLI/TIRS and digital elevation model images (case study: Lawu Mount, Central Java)

Mount Lawu is one of the volcanoes in Central Java Province with hidden geothermal potential. This research aims to identify geothermal potential areas, especially hydrothermal alteration areas, using Landsat 8 imagery in Mount Lawu, Central Java. Hydrothermal alteration serves as a crucial indicator in the exploration of geothermal potential in an area. Multispectral image processing methods of Landsat 8 are employed to map the distribution patterns of hydrothermal alterations in the study area, together with the anomaly of temperature and heatflux in the surface. This approach involves principal component analysis and spectral alteration indications that combine information from various image channels. Lithology and lineament density analysis strengthen the delineation and analysis of the geothermal potential area. The western-northwestern part of Mount Lawu is the most potential region for geothermal resources based on the distribution of hydrothermal alteration areas and the high density of faulted regions, which are also supported by geothermal manifestations such as hotspring sites in the area.


Introduction
Geothermal energy is one of the many natural resources and energy sources that Indonesia is endowed with.The Indonesian archipelago, including Java Island in particular, has a distinct tectonic configuration due to its location in the subduction zone between the Indo-Australian and Eurasian tectonic plates.A chain of volcanoes known as the "Ring of Fire," which stretches from the western to the eastern extremities of Indonesia, results from the flourishing of magmatism brought about by this plate boundary tectonic arrangement.These volcanoes' distribution is well-matched with areas that have the potential for geothermal energy.In a geothermal system, magmatism is an essential heat source.Indonesia's geothermal energy potential ranks among the top four in the world and is second in the world regarding its high-temperature geothermal potential.The total potential geothermal energy of Indonesia (resources and reserves) is estimated to reach 23.060 MW or account for 40% of the world's geothermal reserves, based on data from the Geological Agency, Ministry of Energy and Mineral Resources in December 2022 [1], [2].Exploration and utilization of geothermal energy in Indonesia are essential government programs aimed at promoting the use of renewable energy as a means of energy diversification and environmental conservation.
Situated on the border between the provinces of Central and East Java, Mount Lawu is a stratovolcano that represents one of the geothermal prospective areas that still needs to be thoroughly IOP Publishing doi:10.1088/1755-1315/1339/1/012004 2 studied and utilized to its full potential.As a type A volcano that has erupted at least once since 1600 AD, Mount Lawu is classified as having had its most recent eruption on November 28, 1885.Eruptions from small crater that release fumaroles and solfataras, furthermore the occurences of hot springs, are indicative of active magmatic activity now occurring on Mount Lawu.In the region, Young Mount Lawu and Old Mount Lawu, sometimes called Pra-Lawu, both formed during the Holocene period, are notable for their volcanic activity, which produced the igneous and pyroclastic rocks that make up most of the rocks in the Mount Lawu area.Certain areas around the Mount Lawu area also contain limestone from the Wonosari Formation and volcanic breccia from the Notopuro Formation [3].The geological structure in the Mount Lawu region consists of faults that have formed as a result of tectonic processes due to the interaction of Earth's plates and volcanic processes that developed concurrently or after the eruption of Mount Lawu.A northwest-southeast oriented normal fault is found on the northwest slope of Mount Lawu, a north-south oriented normal fault is found on the central part of Mount Lawu, while a west-east oriented normal fault is found on the southern slope of Mount Lawu [3].
Remote sensing image analysis has become one of the methods used in geothermal research.The use and processing of high-resolution satellite images that can be freely downloaded are essential steps to be carried out before field mapping and checking activities to optimize and make fieldwork efficient, both in terms of time, workforce, and cost.This research utilizes Digital Elevation Model data with an 8-meter spatial resolution downloaded from the Indonesian Geospatial Portal website to perform analyses of lineaments and fractures that serve as paths for hydrothermal fluid movement.The Digital Elevation Model data also serves as the basis for analyzing fracture density and lineament related to the surface manifestations of geothermal activity.The United States Geological Survey (USGS) website provides Landsat 8 imagery that is used to map the temperature anomaly pattern on the surface (Land Surface Temperature/LST) and alteration mineral dispersion that are correlated with vegetation density and the pathway for hot fluids to flow from the Earth's interior to the Earth's surface, which serves as evidence of geothermal manifestations on the surface

Materials
Landsat-8 imagery acquired by the United States Geological Survey (USGS) in August 2023 (https://usgs.gov) and the National Digital Elevation Model (DEM) made available by the Geospatial Information Agency on the Indonesian Geospatial Portal (https://tanahair.indonesia.go.id) are two of the data used in this study.The DEM data, presented in a digital dataset in grid or raster format, displays the elevation or height of the Earth's surface at specific locations and is utilized for specific thematic research projects.A representative National DEM dataset with an 8-meter resolution, used in this study, is generated by processing and compiling data from various sources, including IFSAR, TERRASAR-X, and ALOS PALSAR, complemented by mass point data used in the creation of the Indonesian Land Cover Map (RBI).The DEM data is processed and converted into hillshade images with lighting azimuth angles of 0°, 45°, 90°, and 135°; lighting azimuth angles of 180°, 225°, 270°, and 315°; and a 45° slope of the lighting horizon from the flat plane.The hillshade method is a technique that represents the relief of an area by creating a 3D effect in the image [4].
Landsat-8 imagery is acquired by the Landsat-8 satellite.The Landsat-8 satellite is an Earth observation satellite initiated by NASA (National Aeronautics and Space Administration) with a sunsynchronous polar orbit, an inclination angle of 98.2°, and an altitude of 705 km above the equator.Landsat-8 imagery is equipped with various high-resolution multispectral imaging sensor instruments, such as the Operational Land Imager (OLI) and the Thermal Infrared Sensor, enabling the acquisition of high-resolution images across various wavelengths, including ultraviolet, visible, infrared, and thermal [5].Landsat-8 is capable of producing high-detail images with resolutions ranging from 15 to 100 meters, depending on the wavelength and image channels used.Data from the OLI sensor on Landsat-8, specifically bands 4 (visible red light) and 5 (near-infrared), will be used for land cover classification.Land cover classification is based on the Normalized Difference Vegetation Index (NDVI) values, which depict the vegetation density in a given area [6].
Ground surface temperatures are determined through analysis of the Land Surface Temperature (LST) using data from band 10 on the Thermal Infrared Sensor (TIRS).Landsat-8 imagery for the Gunung Lawu and surrounding areas was acquired by downloading it from the USGS Explorer website.The selected Landsat 8 imagery for download meets the criteria of cloud cover, with less than 10% cloud cover on the images, making it suitable for LST and NDVI analysis using several GIS analysis softwares.

Methods
The research process is divided into four stages, including the processing and analysis of the National Digital Elevation Model (DEM) data for the creation of lineament density maps, the generation of Normalized Difference Vegetation Index (NDVI) maps, surface temperature distribution maps, and thematic multispectral composite maps.

Fault Fracture Density (FFD)
The processing of National DEM data for creating lineament density maps is performed using the Fault and Fracture Density (FFD) method, considering the straightness of valleys and river channels as expressions of surface morphology and geological structural controls.Ridges, hills, and valley straightness are associated with fractures or faults that serve as pathways for the circulation of fluids from reservoirs to the Earth's surface, often marked by the presence of geothermal manifestations, such as hot springs, fumaroles, mud pools, and the alteration rocks [7], [8], [9].Automatic lineament delineation methods using the LINE algorithm by Thannoun (2013) are applied in this study using certain remote sensing and photogrammetry software.Automatic lineament delineation parameters include filter radius, gradient threshold, length threshold, line fitting error threshold, angular difference threshold, and linking distance threshold.The delineated lineaments from DEMNAS data are then grouped into 2 km x 2 km grids to produce lineament density contours.The 2 km grid size is chosen to obtain regional structural density as additional information for geothermal system interpretation.The FFD analysis results in the distribution of areas with high and low lineament density.Areas with high lineament density values are interpreted to represent upflow and outflow areas [4].

Normalized Difference Vegetation Index (NDVI)
Landsat-8 imagery from the visible and near-infrared bands is employed to compute a vegetation index through a transformation process known as NDVI (Normalized Difference Vegetation Index) analysis.The NDVI calculation is critical because the presence of vegetation plays a fundamental role in determining temperature and assessing the extent of vegetation within a specific area.Prior to conducting NDVI calculations, radiometric corrections are applied to bands 4 (visible) and 5 (nearinfrared).NDVI is calculated by measuring the difference in near-infrared (NIR) reflectance and red light (RED) on satellite images or other sensors.The NDVI formula is as follows: NDVI = (NIR -RED) / (NIR + RED) Radiometric correction aims to reduce errors in recording the values of sunlight reflection by converting Digital Numbers (DN) into Top of Atmosphere Reflectance values.The output from the NDVI algorithm yields values within a range of -1 to 1, which are used to represent land cover conditions (vegetation density) in a specific area.The classification of NDVI values for vegetation density analysis [5], as indicated in Table 1.The subsequent stage in calculating NDVI values involves the computation of vegetation proportion values [4], [5].

Land Surface Temperature (LST)
The Land Surface Temperature (LST) value represents the state or condition of the average surface temperature of a region controlled by the balance of surface energy, atmosphere, surface thermal properties, and subsurface soil properties [10].LST is measured from the temperature at the Earth's surface, excluding the atmospheric temperature at the top layer.Thermal images from satellites are processed by calculating thermal radiation and considering factors such as solar radiation, elevation, and land surface properties.LST maps are derived from one of the TIRS (Thermal Infrared Sensor) Landsat-8 bands, specifically band 10, with adjustments involving the OLI (Operational Land Imager) bands, particularly bands used in the red and near-infrared spectral range, which are band 4 and band 5.The steps involved in creating LST maps include the calculation of Top Atmospheric Spectral Radiance, conversion of Radiance to At-Sensor Temperature, computation of Normalized Difference Vegetation Index (NDVI), vegetation proportion, Land Surface Emissivity (LSE), and LST [9].LST analysis is often combined with NDVI calculations to understand the relationship between vegetation and surface temperature.

Results & Analysis
The analysis of DEM images involves eight azimuthal lighting directions, comprising a combination of four directions at 0°, 45°, 90°, and 135°, as well as four directions at 180°, 225°, 270°, and 315°, with a constant illumination horizon angle of 45° from a flat plane.These eight images are further analyzed by merging them and assigning weighted values using the raster calculator tool in certain GIS analysis software.Location data related to Mount Lawu, hot spring locations, and fumaroles in the potential geothermal area of Mount Lawu, are also added to these images [3].The processed DEM images are utilized for automated lineament delineation using certain remote sensing and photogrammetry software and the LINE algorithm.The reference values for the lineament delineation algorithm are based on the standards explained [11].The delineated lineaments are predominantly oriented in the northwest-southeast and northeast-southwest directions, representing valleys and river channels.
The Fracture and Fault Density (FFD) analysis is carried out using the primary shapefile data of the delineated lineaments, processed in certain GIS analysis software with the line density tool.The line density tool calculates density based on the radius and spatial density between lineaments and interpolates regions with similar density values.The lineament density map generated by this tool is further processed to create a clearer and higher-resolution visualization.This density map is overlaid with the lineament shapefile to observe the distribution of high-density areas.Areas with high density are depicted in shades of red and yellow, predominantly located in the northwest and southeast of Mount Lawu (figures 1 and 2).Hot springs are concentrated in the western and northwestern parts of Mount Lawu, as indicated by the high lineament density values.The analysis of Landsat-8 images in this study utilizes four combinations of RGB bands from Landsat-8 images: natural color, false color, and altered rocks.In the natural color image, the red channel is placed in band 4, the green channel in band 3, and the blue channel in band 2. The natural color image displays the true colors of the Earth's surface as seen from the satellite (see Figure 3).In the false color image, the red channel is placed in band 5, the green channel in band 6, and the blue channel in band 7. The false color image displays non-real colors of the Earth's surface from the satellite perspective (see Figure 4).The combination of RGB bands in the false color image makes it easier to interpret surface geological conditions compared to the natural color image.Areas with sparse vegetation are shown in orange and gradually transition to brown in areas with dense vegetation.Human settlements and surface water flow patterns are indicated by light blue, while bodies of water on the Earth's surface are shown in black.Landsat-8 image analysis for determining the distribution of altered rocks on the Earth's surface uses image combinations of band ratios from band 4 and band 2 for the red channel, band ratios from band 6 and band 7 for the green channel, and band 5 for the blue channel [11] (see Figure 5).Areas with dense vegetation are shown in green and transition to light bluish-green in areas with sparse vegetation.Bodies of water on the Earth's surface are represented in dark red, while human settlements and surface water flow patterns are depicted in light pink.The distribution of altered rocks on the surface is indicated by a light bluish-green color.Hot spring manifestations, correlated with the presence of altered rock features on the Earth's surface, are identified from the Landsat-8 image.The area to the west-northwest is depicted with a pale green to brownish color.Cloud cover captured in the Landsat-8 image is shown in dark purple.The Normalized Difference Vegetation Index (NDVI) value depicts how dense the vegetation in a particular area is [6].NDVI values range from -0.754499 to 0.868756.On the NDVI map, areas indicated by dark green colors represent higher NDVI values, while lighter green areas indicate lower NDVI values (see Figure 6).Areas with high NDVI values reflect denser vegetation because the surface of the vegetation reflects more radiation in the infrared spectrum compared to the visible light spectrum.Areas with lower NDVI values indicate lower vegetation density and/or the presence of water bodies and cloud cover.One limitation of NDVI analysis is that it cannot be solely relied upon to distinguish areas with dense vegetation from those without, as there is no consistent NDVI threshold value due to its variability across seasons [9].
The maximum land surface temperature value falls within the range of 42.26 °C and is represented in red on the LST map (see Figure 7).Regions with the minimum land surface temperature have values as low as 6.

Conclusions & Suggestions
The FFD analysis, based on the delineation of valley lineaments showing zones of tectonic activity from predominantly northwest-southeast and northeast-southwest-oriented DEM data, is used.Landsat-8 imagery processing is employed to approximate values for vegetation cover thickness, residential area extent, and water infiltration areas concerning the distribution of alteration rocks on the Earth's surface.Surface temperature values ranging from 42.26 °C to 6.4 °C, correlated with processed and adjusted vegetation density values and the distribution of alteration rocks, form the basis for assessing geothermal surface manifestation zones.Because the sensor cannot penetrate clouds and may impact the quality of temperature processing and vegetation density categorization, we advise utilizing Landsat data that is cloudy.Geological, geophysical, and geochemical research on geothermal potential sites is necessary to produce the geothermal potential.

Figure 3 .
Figure 3. Composite map based on natural color Landsat-8 imagery of Mount Lawu.

Figure 4 .
Figure 4. Composite map based on false color Landsat-8 imagery of Mount Lawu.
4 °C and are depicted in blue.This minimum land surface temperature corresponds to the surface covered by clouds near the Earth's surface to the north of the central peak of Mount Lawu.Recorded land surface temperature values indicate the temperature of the outermost layer of an object.Land surface temperature represents the temperature at the outermost layer of the Earth's surface.At the same time, for vegetation, such as forests, it can be interpreted as the temperature of the vegetation cover or the temperature of the surface water within aquatic objects.High vegetation density leads to lower land surface temperature values, as observed in the central and medial zones of Mount Lawu.The NDVI map is linked to the LST map, revealing that geothermal manifestations are found in areas with sparse to moderate vegetation and relatively high surface temperature values.The high surface temperature values in geothermal manifestation areas are due to the emergence of hot fluids from beneath the Earth's surface.