Mapping the structure of forest vegetation with field surveys or high-resolution light detection and ranging (LiDAR) data is costly. We tested whether landscape topography and underlying geology 10 could predict the vegetation density of a 19 km² area of wet eucalypt forest at the Warra Long-Term Ecological Research Supersite, Tasmania, Australia. Using spatial layers for 12 topographic attributes derived from digital terrain models (DTMs) and a geology layer, we predicted the vegetation density of three strata with a high degree of accuracy (validation root mean square error ranged from 9.0% to 13.7%). The DTMs with 30 m resolution provided greater predictive accuracy than DTMs with higher 15 resolution. The importance of different variables depended on spatial resolution and strata. Among the predictor variables, geology generally had the highest predictive importance, followed by solar radiation. Topographic Position Index, aspect, and System for Automated Geoscientific Analyses Wetness Index had moderate importance. This study demonstrates that geological and topographic attributes can provide useful predictions for the density of vegetation layers in a tall wet sclerophyll 20 primary forest. Given the good performance of the model based on 30 m DTM resolution, the predictive power of the models could be tested on a larger geographical area using lower-density Q2 LiDAR point clouds combined with medium-resolution satellite data.

Item Type:	Refereed Article
Keywords:	topography, understory, LiDAR, forest, layers, DEM, terrain, wet eucalypt forest, airborne LiDAR, digital terrain model, topographic attributes, geology, vegetation density, random forest, variable importance
Research Division:	Engineering
Research Group:	Geomatic engineering
Research Field:	Photogrammetry and remote sensing
Objective Division:	Environmental Management
Objective Group:	Terrestrial systems and management
Objective Field:	Assessment and management of terrestrial ecosystems
UTAS Author:	Yadev, BKV (Dr Bechu Yadav)
UTAS Author:	Lucieer, A (Professor Arko Lucieer)
UTAS Author:	Jordan, GJ (Professor Greg Jordan)
UTAS Author:	Baker, SC (Associate Professor Sue Baker)
ID Code:	148014
Year Published:	2022
Funding Support:	Australian Research Council (LP140100075)
Deposited By:	Geography and Spatial Science
Deposited On:	2021-11-30
Last Modified:	2022-12-08
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