Landscape-wide modelling of canopy tree crowns and heights using LiDAR: a case study in the Northern Rivers of New South Wales, Australia
P. Norman


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Abstract
Understanding the extant structure of forests reveals important insights into their ecological condition, age, biodiversity and related ecosystem services. Advances in LiDAR and computational power enable detailed assessments of forest structure at an individual tree resolution over large geographic extents.
This study aimed to model and map tree canopy crowns and heights at a landscape scale and investigate the influence of forest type, land use and tenure, and environmental factors on spatial variation in forest height.
We utilised publicly available Airborne Laser Scanning (ALS) data to model canopy shape and height for individual trees, across a 3.1 Mha study region in the Northern Rivers region of New South Wales, Australia, employing LiDAR-derived Canopy Height Model (CHM) and Dalponte crown segmentation techniques. Tree heights were subsequently compared between different vegetation formations and stratified by land use and tenure.
A total of 180,709,102 tree crowns was identified. The tallest trees included a 81 m tall Eucalyptus grandis specimen and a 77 m tall Araucaria cunninghamii specimen. The analysis of tree heights among vegetation formations and land use/tenure revealed that tree heights were tallest in wet sclerophyll forest, and Nature Conservation and Production Native Forest tenures. Tree crown detection accuracy was high (2.3% difference), although discrepancies were noted in areas affected by severe fires and complex rainforest canopies.
The results show that LiDAR and advanced modelling techniques can be applied to model map forest canopy structure on an individual tree basis at a landscape scale.
These results provide valuable insights into the ecological condition of the region’s forests that can inform management strategies and conservation efforts. The methods can be readily applied to other forested landscapes where airborne LiDAR is available.
Keywords: ALS, drones, forest mapping, forests, LiDAR, old-growth, remote sensing, tree height.
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