Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning
Article first published online: 30 JAN 2014
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 5, Issue 3, pages 263–272, March 2014
How to Cite
Ashcroft, M. B., Gollan, J. R., Ramp, D. (2014), Creating vegetation density profiles for a diverse range of ecological habitats using terrestrial laser scanning. Methods in Ecology and Evolution, 5: 263–272. doi: 10.1111/2041-210X.12157
- Issue published online: 11 MAR 2014
- Article first published online: 30 JAN 2014
- Manuscript Accepted: 16 DEC 2013
- Manuscript Received: 4 JUN 2013
- Environmental Trust. Grant Number: 2011/RD/0099
- Australian Research Council. Grant Number: LP100200080
- University of Technology Sydney
- canopy cover;
- habitat heterogeneity;
- leaf area index;
- vegetation structure
- Vegetation structure is an important determinant of species habitats and diversity. It is often represented by simple metrics, such as canopy cover, height and leaf area index, which do not fully capture three-dimensional variations in density. Terrestrial laser scanning (TLS) is a technology that can better capture vegetation structure, but methods developed to process scans have been biased towards forestry applications. The aim of this study was to develop a methodology for processing TLS data to produce vegetation density profiles across a broader range of habitats.
- We performed low-resolution and medium-resolution TLS scans using a Leica C5 Scanstation at four locations within eight sites near Wollongong, NSW, Australia (34·38–34·41°S, 150·84–150·91°E). The raw point clouds were converted to density profiles using a method that corrected for uneven ground surfaces, varying point density due to beam divergence and occlusion, the non-vertical nature of most beams and for beams that passed through gaps in the vegetation without generating a point. Density profiles were evaluated against visual estimates from three independent observers using coarse height classes (e.g. 5–10 m).
- TLS produced density profiles that captured the three-dimensional vegetation structure. Although sites were selected to differ in structure, each was relatively homogeneous, yet we still found a high spatial variation in density profiles. There was also large variation between observers, with the RMS error of the three observers relative to the TLS varying from 16·2% to 32·1%. Part of this error appeared to be due to misjudging the height of vegetation, which caused an overestimation in one height class and an underestimation in another.
- Our method for generating density profiles using TLS can capture three-dimensional vegetation structure in a manner that is more detailed and less subjective than traditional methods. The method can be applied to a broad range of habitats – not just forests with open understoreys. However, it cannot accurately estimate near-surface vegetation density when there are uneven surfaces or dense vegetation prevents sufficient ground returns. Nonetheless, TLS density profiles will be an important input for research on species habitats, microclimates and nutrient cycles.