Using topographic attributes to predict the density of vegetation layers in a wet eucalypt forest

ABSTRACT

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 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 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 (SAGA) 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 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 LiDAR point clouds combined with medium-resolution satellite data.

KEYWORDS:

• wet eucalypt forest
• airborne LiDAR
• digital terrain model
• topographic attributes
• geology
• vegetation density
• random forest
• variable importance

Acknowledgements

We acknowledge the Terrestrial Ecosystem Research Network (TERN) and Airborne Research Australia (ARA) for funding and collecting airborne LiDAR data. This study was partially funded by the Australian Research Council Linkage Project LP140100075 with industry partners Sustainable Timber Tasmania and VicForests. Bechu Yadav was supported by a Tasmania Graduate Research Scholarship from the University of Tasmania. Thanks to Timothy Wardlaw for advice on study conception. We would like to thank the anonymous reviewers for their comments and efforts towards improving our manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.