Advances in Mapping Forest Biomass and Old-Growth Conditions Using Waveform Lidar

Abstract

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne waveform lidar sys- tem that has transformed scientific understanding of the world’s forests through billions of pre- cise measurements of ecosystem structure. Relative to forest processes that operate on decadal to millennial timescales, the four year period during which GEDI collected these measurements is short, and GEDI’s ability to analyze how forest structure changes over time is mostly unproven. However, fusion efforts that integrate GEDI data with forest inventory measurements and ecosys- tem models hold immense potential for discovery. In this dissertation, I explore the limitations and capabilities of GEDI data for inference into structural and successional dynamics within east- ern US forests. First, I used a forest gap model to quantify uncertainty in biomass predictions for individual GEDI waveforms, and discovered a relationship between biomass uncertainty and successional stage. Next, I investigated uncertainties and errors in large-scale GEDI biomass estimates relative to unbiased estimates from the US forest inventory. I developed a novel mod- eling framework based on fusion between GEDI and the US forest inventory data that corrected these errors, and I produced unbiased and precise maps of forest biomass for the continental US. Lastly, I assessed GEDI’s ability to identify and map different types of old-growth forests, and discovered that GEDI can detect some old forests more effectively than others. This research identified key limitations associated with using GEDI to study forest dynamics, and I leveraged these discoveries to develop new ways of using GEDI data for ecological and successional in- ference. These discoveries will inform new uses of GEDI data and its integration with inventory data and ecosystem modeling to better characterize changes within forest ecosystems.