CHARACTERIZING STRUCTURAL COMPLEXITY OF THE EARTH’S FORESTS WITH GEDI

Abstract

This dissertation helps advancing the characterization, mapping, and monitoring of forest structural complexity through spaceborne remote sensing centered on lidar technology. It develops three interconnected methodologies that collectively contribute to our ability to assess forest structure across spatial scales. First, I introduce the Waveform Structural Complexity Index (WSCI), derived from the relationship between airborne laser scanning measurements and GEDI spaceborne lidar waveforms, enabling global mapping of actual forest structural complexity, which reveals distinct biome-specific patterns, with tropical forests exhibiting consistently higher canopy complexity than temperate forests. Second, I address GEDI's sampling limitations by developing a computationally efficient deep learning framework that fuses GEDI WSCI estimates with synthetic aperture radar data to produce continuous high-resolution (25m) maps of structural complexity across global forests. This approach demonstrates robust performance across diverse biomes and time periods, while providing calibrated estimates of structural complexity and pixel-level uncertainty. Finally, I apply this fusion dataset to examine forest responses to fire disturbances in a protected area in the Amazon, revealing that fire effects extend up to 2200m into undisturbed forests, with structural complexity showing higher sensitivity to edge effects from fire disturbances than canopy height. These findings collectively contribute to our understanding of forest structural complexity across scales and establish new methodologies for monitoring forest ecosystems in an era of rapid environmental change, while providing tools for supporting forest conservation, management, and research globally.