Summary

Description

HyPerspectives, Inc. and its researchers propose to use remote sensing technologies to answer specific scientific questions for researchers and decision-makers in the natural hazards community. We will employ both current and innovative data fusion techniques to fill key deficiency gaps limiting progress in the natural hazards discipline. By fusing high-resolution hyperspectral imagery and LiDAR (Light Detection And Ranging) data sets from the 2003 Yellowstone Optical and SAR Ground Imaging (YOGI) data collect, we will substantially improve methodologies for natural hazard decision support systems. This imagery fusion is considered innovative because it will further refine the identification and mapping of past events, such as landslides, while also providing quicker and simpler processes for forecasting and mitigating future hazards. Furthermore, the algorithms developed in Phase 2 will satisfy the needs of decision makers by including tools for fault detection, deformation, and geothermal monitoring. The proposed study is directly relevant to the NASA SBIR S7.01 solicitation because we will create automated tools utilizing innovative algorithms to speed up the processing of data that has known relevance to natural hazard planners and researchers. To achieve this, we will build on successful landslide detection techniques and incorporate new algorithms previously developed by HyPerspectives scientists.