Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran

摘要

Land surface temperature (LST) and soil moisture are important factors in environmentalhazard modeling. The main objective of this research is to derive the LST and a soil moistureindex (SMI) from thermal satellite images. A split-window algorithm is applied to derivethe spectral radiance and emissivity from two thermal infrared (TIR) bands of the Landsat 8satellite in four consecutive years (2015–2018) to serve as input for the LST analysis. First,the normalized difference vegetation index (NDVI) is computed from which an emissivityindex is calculated using an object-based threshold technique. This is followed by the calculationof the LST via a split-window algorithm. Subsequently, the SMI is modeled to reflectthe relationship between the surface temperature and the vegetation cover. A spatial analysisinvestigates the relationship between the LST and SMI with known geological faults.The results indicate that the areas with low-temperature and high-moisture overlap withfault zones. The authors discuss to what degree fault zones can be detected or predictedbased on LST and SMI.