Open AccessEstimation of land surface temperature in Dieng volcanic complex using tir-based satellite imageries
Author(s) -
Astisiasari,
Dyah Rahmawati Hizbaron,
Muhammad Anggri Setiawan
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/451/1/012066
Subject(s) - advanced spaceborne thermal emission and reflection radiometer , remote sensing , satellite , environmental science , volcano , thermal infrared , thermometer , pixel , infrared , meteorology , geology , geography , physics , digital elevation model , optics , quantum mechanics , astronomy , seismology
The satellite-based Land Surface Temperature (LST) and its anomaly values were observed in the geothermal area of Dieng Volcanic Complex (DVC). Landsat-8 and ASTER product on surface kinetic temperature (AST-08) were selected, based on its spectral and spatial resolution characteristics, as well as its retrieval methods applied for the LST extraction. Statistical analysis was performed on the pixel-integrated temperatures to cross-compare both of the remote sensing-based LST. Thermal Infrared (TIR)-based LST was also correlated with the LST reference from a field measurement. Correlation between the field measurement and satellite-based LST was considered insignificantly weak; while correlation between the satellite-based LST (Landsat-8 with SW and ASTER-based with TES) was significantly strong. The LST registration approaches cause difference on correlation strength. Field-based LST directly measured the ground kinetic temperature of sample points with infrared thermometer; while satellite-based LST sensed the thermal radiation emitted by the represented objects in a pixel area that possibly mixed the LST values. The clusters in response to geothermal features reflected bias result; as the thermal anomalies were less pronounced, observed from both of the satellite-based LST estimation.