| Title: |
Using topographic attributes to predict the density of vegetation layers in a wet eucalypt forest |
| Authors: |
Yadev, BKV; Lucieer, A; Jordan, GJ; Baker, SC |
| Publisher Information: |
Taylor & Francis Australasia |
| Publication Year: |
2022 |
| Subject Terms: |
Engineering; Geomatic engineering; Photogrammetry and remote sensing; geo; envir |
| Description: |
Mapping the structure of forest vegetation with field surveys or high-resolution light detection andranging (LiDAR) data is costly. We tested whether landscape topography and underlying geology10 could predict the vegetation density of a 19 km 2 area of wet eucalypt forest at the Warra Long-TermEcological Research Supersite, Tasmania, Australia. Using spatial layers for 12 topographic attributesderived from digital terrain models (DTMs) and a geology layer, we predicted the vegetation densityof three strata with a high degree of accuracy (validation root mean square error ranged from 9.0% to13.7%). The DTMs with 30 m resolution provided greater predictive accuracy than DTMs with higher15 resolution. The importance of different variables depended on spatial resolution and strata. Amongthe predictor variables, geology generally had the highest predictive importance, followed by solarradiation. Topographic Position Index, aspect, and System for Automated Geoscientific AnalysesWetness Index had moderate importance. This study demonstrates that geological and topographicattributes can provide useful predictions for the density of vegetation layers in a tall wet sclerophyll20 primary forest. Given the good performance of the model based on 30 m DTM resolution, thepredictive power of the models could be tested on a larger geographical area using lower-densityQ2 LiDAR point clouds combined with medium-resolution satellite data. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://doi.org/10.1080/00049158.2021.2004687 |
| DOI: |
10.1080/00049158.2021.2004687 |
| Availability: |
https://doi.org/10.1080/00049158.2021.2004687 |
| Rights: |
undefined |
| Accession Number: |
edsbas.AC74019F |
| Database: |
BASE |