Shortwave infrared (SWIR) spectroscopy can be applied directly to analyze the mineral chemistry of raw or geologic materials. It provides diagnostic spectral characteristics of the chemical composition of minerals, information that is invaluable for the identification and quality control of such materials. The present study aims to investigate the potential of SWIR spectroscopy as an alternative quality control technique for the mineral chemistry analysis of Portland cement-grade limestone. We used the spectroscopic (wavelength position and depth of absorption feature) and geochemical characteristics of limestone samples to estimate the abundance and composition of carbonate and clay minerals on rock surfaces. The depth of the carbonate (CO₃) and Al-OH absorption features are linearly correlated with the contents of CaO and Al₂O₃ in the samples, respectively, as determined by portable X-ray fluorescence (PXRF) measurements. Variations in the wavelength position of CO₃ and Al-OH absorption features are related to changes in the chemical compositions of the samples. The results showed that the dark gray and light gray limestone samples are better suited for manufacturing Portland cement clinker than the dolomitic limestone samples. This finding is based on the CaO, MgO, Al₂O₃, and SiO₂ concentrations and compositions. The results indicate that SWIR spectroscopy is an appropriate approach for the chemical quality control of cement raw materials.

Seulawah Agam, a stratovolcano located in the Aceh province, Indonesia, has not erupted for a long decade after the last eruption in 1839. Thermal infrared remote sensing has been used to determine the land surface temperature (LST) of the volcano area. However, the application of remotely sensed thermal imagery in identifying the LST of the Seulawah Agam volcano, as a precursor of geothermal energy and eruption hazard, has not been completely monitored. The volcano locates relatively close to residential areas, which is a challenging approach to apply thermal bands in determining geothermal identities. In this research, we assess the LST and vegetation index for the detection of the thermal activity of the mountain. These characteristics were retrieved from Landsat 7 ETM+ and Landsat 8 TIRS/OLI imageries, acquired on 23 April 2004 and 16 March 2015 over the Seulawah Agam area, respectively. The normalized difference vegetation index (NDVI) threshold method for emissivity retrieval and split-window algorithm for land surface temperature (LST) were utilized. The results show that the vegetation index changes moderately over the geothermal area, especially at the residential area and western side of the volcano which is in line with a fault structure of the Seulimeum segment. We calculated the LST from the thermal bands of Landsat images 2004 and 2015 with approximate results are 28 – 35 °C. The spatial distribution of surface temperatures at the mountain derived from the classified image 2015 varies considerably compared with the classified image 2004. The surface temperature and vegetation index changes indicate a thermal activity at the Seulawah Agam volcano. It can be concluded that the Landsat 7 ETM+ and Landsat 8 TIRS/OLI imageries are potentially used to study the thermal status of the Seulawah Agam geothermal area.

Geothermal energy, as a part of green and renewable energy, has been widely developed in the world to replace the current conventional fossil energy. Peut Sagoe is an active volcano in the northern part of Sumatra. The volcanic mountain has not been completely explored for geothermal and energy reserves study. This is due to the volcano locates in a high topography and surrounded by dense tropical forest, which makes it challenging to deploy geophysical instruments in the area. The Landsat 8 thermal infrared and meteorological data from 2013 – 2020 were used to estimate the energy resources by calculating the radiative heat flux (RHF) and measuring the energy lost annually through the heat discharge rate (HDR). We also used the normalized differential vegetation index (NDVI) for vegetation analysis, and estimation of its emissivity data. The mono-window algorithm was used to calculate the land surface temperature (LST). The Stefan–Boltzmann equation was utilized to analyze thermal infrared data for RHF, and ambient temperature and relative humidity data were acquired from the Indonesian Meteorological Agency (BMKG) database. The results showed that low vegetation values and high LST of 25°C–35°C were found in crater areas, which indicate the underground thermal activities of the mountain. It demonstrates that the maximum RHF values were 55 W/m2 in 2013 and 37 W/m2 in 2020. The HDR data were calculated by applying 15% of the RHF data, and the amounts of energy lost were 132.5 MWe and 64.5 MWe in 2013 and 2015 respectively. It increased to 186.4 MWe in 2017 and 89 MWe in 2020. Based on these predicted results, we conclude that the combination of thermal infrared imagery of Landsat 8 and meteorological data is an effective approach in estimating geothermal energy potential and energy loss of volcanoes situated in remote areas