Journal article
Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in Eucalyptus regnans Forest
Dominik Jaskierniak, George Kuczera, Richard Benyon, Luke Wallace
REMOTE SENSING | MDPI | Published : 2015
DOI: 10.3390/rs70607298
Abstract
Managers of forested water supply catchments require efficient and accurate methods to quantify changes in forest water use due to changes in forest structure and density after disturbance. Using Light Detection and Ranging (LiDAR) data with as few as 0.9 pulses m-2, we applied a local maximum filtering (LMF) method and normalised cut (NCut) algorithm to predict stocking density (SDen) of a 69-year-old Eucalyptus regnans forest comprising 251 plots with resolution of the order of 0.04 ha. Using the NCut method we predicted basal area (BAHa) per hectare and sapwood area (SAHa) per hectare, a well-established proxy for transpiration. Sapwood area was also indirectly estimated with allometric r..
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Grants
Awarded by Australian Research Council
Funding Acknowledgements
This project was funded by Melbourne Water and an Australian Research Council Linkage Grant (LP110200194). To undertake the computation of the NCut algorithm, Victorian Life Sciences (VLSCI) computation facility provided a resource grant (VR0286) under the Resource Allocation Scheme (RAS). We thank Claire Ren for her efforts in delineating some of the stumps and assistance in the field. We also thank the anonymous reviewers for their advice on improving the quality of this paper.