MONITORING THE 2018 ERUPTION OF KĪLAUEA VOLCANO USING VARIOUS REMOTE SENSING TECHNIQUES

Monitoring the regions that are prone to natural hazards is essential in disaster management to provide early warnings. Airborne and space-borne remote sensing techniques are cost-effective in accomplishing this task. Interferometric Synthetic Aperture Radar (InSAR) is an advanced remote sensing technique used to detect and measure the changes in the Earth’s topography over time. Spaceborne InSAR is a precise (~mm accuracy) way to measure the land surface altitudinal changes. Persistent Scatterer Interferometry (PSI) is a powerful method of differential SAR interferometry that processes the InSAR data by automatically selecting the persistent scatterers in the region. In this thesis, I developed a new algorithm to estimate the areal coverage and volume of newly erupted lava by integrating the space-borne InSAR, thermal infrared, Light Detection and Ranging (LiDAR), and Normalized Difference Vegetation Index (NDVI) techniques. I applied this algorithm to the eruption of the East Rift Zone (ERZ) of the Kīlauea volcano that took place between May and August 2018 as a case study, and estimated the areal coverage and volume of lava erupted. I compared the results of InSAR to those derived from airborne LiDAR. I found that although air-borne LiDAR provides data with higher resolution and accuracy, InSAR is almost as good as LiDAR in monitoring deformed areas and has larger spatial and temporal coverage. I also performed the PSI analysis using the Stanford Method for Persistent Scatterers (StaMPS) algorithm, and determined the Line-of-Sight (LOS) deformations prior, during, and after the 2018 eruption of the Kīlauea volcano. Results from the PSI processing show regional subsidence on the Big Island, indicating the deflation of the southern and western part of the Big Island during the eruption at the East Rift Zone. Keywords: Kilauea