Long-term recovery of canopy 3D structural diversity following wildfires in the world’s largest temperate woodland

TL;DR

This study examines how the 3D structure of a large temperate woodland recovers over 450 years after wildfires, revealing distinct patterns in canopy height, density, and heterogeneity.

Contribution

The study provides the first long-term analysis of canopy structural recovery after wildfires using airborne laser scanning data over a 450-year period.

Findings

Canopy height and density increased rapidly in the first 100–150 years after fire, followed by a plateau in height and gradual decrease in cover.

Old-growth woodlands developed open, spatially heterogeneous structures after several centuries of recovery.

Structural recovery patterns were highly predictable, enabling the development of models to estimate stand age from remote sensing data.

Abstract

Wildfires play a major role in shaping the structure and dynamics of many woody ecosystems, with growing concerns that their frequency and intensity are increasing with climate change. However, we lack an understanding of how canopy structure recovers after wildfires, which limits our ability to forecast the long-term impacts of these disturbances on key ecosystem functions such as carbon storage and biodiversity. Using airborne laser scanning data acquired across a 450 year chronosequence of time since fire, we modelled the recovery trajectory of canopy 3D structural diversity across the largest temperate woodland on Earth in Western Australia. We found that canopy height, cover and heterogeneity recovered at varying rates and followed distinct trajectories. Canopies became taller, denser and more vertically homogeneous during the initial 100–150 years following fire. Subsequently, height growth plateaued while canopy cover continuously decreased for several centuries, leading to open and spatially heterogeneous structures in old-growth woodlands. The highly predictable nature of these structural recovery trajectories following wildfires allowed us to develop robust models for mapping stand age based on structural features. Our study paves the way for leveraging emerging remote sensing technologies to track ecosystem recovery from disturbance, thereby guiding management and restoration interventions at scale.

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