Capturing how forest canopies respond and recover from disturbances using airborne laser scanning

Abstract

Forests are structurally complex ecosystems that cover approximately 30% of the terrestrial land mass and play a crucial role in driving the terrestrial carbon cycle. This diversity and heterogeneity in forest structure is also key to supporting biodiversity and maintains ecosystems functioning. However, both natural and anthropogenic disturbances threaten to alter the structure and function of forests worldwide. Emerging remote sensing technologies are advancing our ability to precisely measure forest structure and disturbance across broad time scales and landscapes. This thesis aims to enhance our current understanding of how disturbances and subsequent recovery processes shape the structure of forest canopies, focusing specifically on airborne laser scanning (ALS, also known as LiDAR) as a powerful tool to characterize canopy 3D structure and dynamics. Chapter 1 synthesizes the drivers that shape forest structure and provides an overview of our current understanding of canopy structural complexity and dynamics. Chapter 2 explores how logging at different intensities impacts the number, size, spatial configuration and geometry of tropical forest canopy gaps. Chapter 3 uses repeated airborne LiDAR data collected across an intact old-growth tropical rainforest landscape to capture how forest structural complexity and canopy dynamics shift along topo-edaphic gradients. Chapter 4 extends the focus of the thesis beyond closed-canopy forests and proposes a set of simple and complementary structural metrics that should allow robust assessments of forest structure in open-canopy ecosystems dominated by fire disturbances. Chapter 5 uses these robust metrics of canopy structure to track the long-term recovery trajectories of canopy 3D structure following stand-replacing fires in the world’s largest temperate woodland. Finally, Chapter 6 reflects back on the main findings of the thesis and discusses current limitations and opportunities for future work.

Date of Award	10 Dec 2024
Original language	English
Awarding Institution	University of Bristol
Supervisor	Tommaso Jucker (Supervisor)