Wildlife Research Wildlife Research Society
Ecology, management and conservation in natural and modified habitats
RESEARCH ARTICLE

Remote sensing can locate and assess the changing abundance of hollow-bearing trees for wildlife in Australian native forests

Christopher J. Owers A B E , Rodney P. Kavanagh C D and Eleanor Bruce A
+ Author Affiliations
- Author Affiliations

A Geocoastal Research Group, Madsen Building F09, University of Sydney, NSW 2006, Australia.

B School of Earth and Environmental Science, Building 41, University of Wollongong, NSW 2522, Australia.

C Forest Science Centre, NSW Department of Primary Industries, PO Box 100, Beecroft, NSW 2119, Australia.

D Niche Environment and Heritage, PO Box W36 Parramatta, NSW 2150, Australia.

E Corresponding author. Email: cjo766@uowmail.edu.au

Wildlife Research 41(8) 703-716 https://doi.org/10.1071/WR14168
Submitted: 19 August 2014  Accepted: 17 February 2015   Published: 14 April 2015

Abstract

Context: Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and diversity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest.

Aims: The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife.

Methods: The data were high-resolution, multispectral aerial imagery and light detection and ranging (Lidar). A ground-based survey of 194 trees, 96 Eucalyptus crebra and 98 E. chloroclada and E. blakelyi, were used to train and validate tree-senescence classification models.

Key results: We found that trees in the youngest stage of tree senescence, which had a very low probability of hollow occurrence, could be distinguished using multispectral aerial imagery from trees in the later stages of tree senescence, which had a high probability of hollow occurrence. Independently, the canopy-height model used to estimate crown foliage density demonstrated the potential of Lidar-derived structural parameters as predictors of senescence and the hollow-bearing status of individual trees.

Conclusions: This study demonstrated a ‘proof of concept’ that remotely sensed tree parameters are suitable predictor variables for the hollow-bearing status of an individual tree.

Implications: Distinguishing early stage senescence trees from later-stage senescence trees using remote sensing offers potential as an efficient, repeatable and cost-effective way to map the distribution and abundance of hollow-bearing trees across the landscape. Further development is required to automate this process across the landscape, particularly the delineation of tree crowns. Further improvements may be obtained using a combination of these remote-sensing techniques. This information has important applications in commercial forest inventory and in biodiversity monitoring programs.

Additional keywords: biodiversity monitoring, forest management, habitat structure, Lidar, multispectral imagery, tree hollows, tree senescence, wildlife conservation.


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