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

Abstract

Land surface temperature (LST) and soil moisture are important factors in environmental hazard modeling. The main objective of this research is to derive the LST and a soil moisture index (SMI) from thermal satellite images. A split-window algorithm is applied to derive the spectral radiance and emissivity from two thermal infrared (TIR) bands of the Landsat 8 satellite 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 emissivity index is calculated using an object-based threshold technique. This is followed by the calculation of the LST via a split-window algorithm. Subsequently, the SMI is modeled to reflect the relationship between the surface temperature and the vegetation cover. A spatial analysis investigates the relationship between the LST and SMI with known geological faults. The results indicate that the areas with low-temperature and high-moisture overlap with fault zones. The authors discuss to what degree fault zones can be detected or predicted based on LST and SMI.