Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

Wilkes, P 2015, Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area, Doctor of Philosophy (PhD), Mathematical and Geospatial Sciences, RMIT University.

Document type: Thesis
Collection: Theses

Attached Files
Name Description MIMEType Size
Wilkes.pdf Thesis application/pdf 5.36MB
Title Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area
Author(s) Wilkes, P
Year 2015
Abstract Assessment of vegetation over large, remote and inaccessible areas is an ongoing challenge for land managers in Australia and around the world. This research aimed to develop metrics, techniques and acquisition specifications that are suitable for characterising vegetation across large forested areas. New methods were therefore required to be transferable between forest types as well as robust where forest structure is unknown a priori. Remote sensing techniques were utilised as they have been previously identified as key in forest assessment, owing to their synoptic capture as well as relative cost. Additionally, active remote sensing instruments, such as LiDAR, are capable of sensing 3-dimensional canopy structure.

Canopy height and the canopy height profile are fundamental descriptors of forest structure and can be used for estimating biomass, habitat suitability and fire susceptibility. To investigate the ability of remote sensing to characterise vegetation structure across large areas, three key research questions were formulated:

I. Which metrics of canopy height and vertical canopy structure are suitable for application across forested landscapes?

II. What is the appropriate ALS sampling frequency for attribution of forest structure across different forest types?

III. How can plot level estimates of canopy structure be scaled to generate continuous large area maps?

A number of inventory measured canopy height metrics were compared with LiDAR analogues, these were shown to be accurate at estimating canopy height and transferable between forest types. Existing techniques for attributing the canopy height profile were found to be inadequate when applied across heterogeneous forests. Therefore a new technique was developed that utilised a nonparametric regression of LiDAR derived gap probability that identified major canopy features e.g. dominant canopy strata and shade tolerant layers beneath.

The impact of sampling frequency was assessed using three key descriptors of canopy structure at six sites across Australia covering a range of forest types. The research concluded that forest structure can be adequately characterised with a pulse density of 0.5 pulses m-2 when compared to a higher density acquisition - 10 pulses m-2. At pulse density of <0.5 pulses m-2, the inability to generate an adequate ground surface model lead to poor results, particularly in high biomass forest. The outcomes of this research will allow land managers to specify lower pulse densities when commissioning LiDAR capture, which may result in significant cost savings.

Finally, LiDAR derived plot estimates were scaled to an area of 2.9 million hectares of forest, where forest type ranged from short, open woodland to tall, closed canopy rainforest. Attribution was achieved using a two-stage sampling approach utilising the ensemble regression technique Random Forest. Predictor variables included freely available datasets such as Landsat TM and MODIS satellite imagery. Canopy height was estimated with a RMSE of 30% or ~5.5 m when validated with an independent forest inventory dataset. Attribution of the canopy height profile was less successful for a number of reasons, for example, the relatively high spatial variability of shade tolerant vegetation. Inclusion of additional synoptic datasets, such as radar, may improve this in the future.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Mathematical and Geospatial Sciences
Subjects Forestry Management and Environment
Keyword(s) Forest
Remote sensing
Land management
Forest structure

Copyright © 2015 by Phil Wilkes

The following wording is taken from the front cover of this thesis:

"to obtain the Double-Badged Degree of Doctor at the University of Twente, Enschede, The Netherlands; and RMIT University, Melbourne, Australia"

"on account of the decision of the Joint Graduation Committee, to be publicly defended on 13th day of January 2016 at 16:45 hrs"

Version Filter Type
Access Statistics: 293 Abstract Views, 308 File Downloads  -  Detailed Statistics
Created: Wed, 20 Apr 2016, 11:52:46 EST by Keely Chapman
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us