Journal article

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar

Melissa Fedrigo, Glenn J Newnham, Nicholas C Coops, Darius S Culvenor, Douglas K Bolton, Craig R Nitschke

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING | ELSEVIER | Published : 2018

Abstract

Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the regi..

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Grants

Funding Acknowledgements

This research was undertaken with approval by the Victorian Department of Environment, Land, Water and Planning (DELWP) under research permit numbers 10006440 and 10006691. The work was funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sustainable Agriculture Flagship program as well as the Victorian Department of Environment, Land, Water and Planning (DELWP) integrated Forest Ecosystem Research program. Additional funding was provided by the Overseas Research Experience Scholarship from the University of Melbourne to facilitate a visiting scholar position in the Integrated Remote Sensing Studio (IRSS) at the University of British Columbia. We thank James Cook University, CSIRO, and the Queensland Department of Science, Information Technology and Innovation (DSITI) Remote Sensing Centre for time allowed to complete this manuscript. We would like to thank those who contributed to data collection and processing, including Benjamin Smith, Stephen B. Stewart, and Gregor Sanders. We would also like to acknowledge Dr. Sabine Kasel, Dr. Lauren T. Bennett, Dr. Stephen H. Roxburgh (CSIRO), Dr. Randall Donohue (CSIRO), Dr. Phil Wilkes (UCL), and Dr. Nicholas Goodwin (DSITI) for their contributions. We also thank all anonymous reviewers for their comments on this manuscript.