Niva Kiran Verma

Above-ground Biomass and Carbon Determination in Farmscapes Using High Resolution Remote Sensing

Niva Verma Conf2012
University
University of New England
Supervisor (Academic)
Prof David Lamb & A/Prof Nick Reid, University of New England
Supervisor (Industry)
A/Prof Brian R Wilson, DECCW NSW
Projects
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Employment
Research Fellow, University of New England
Thesis Abstract

‘Farmscapes’ are farming landscapes that comprise combinations of forests and scattered remnant vegetation (trees), natural and improved grasslands and pastures and crops. Scattered eucalypt trees are a particular feature of Australian farmscapes. There is a growing need to assess carbon and biomass stocks in these farmscapes in order to fully quantify the carbon storage change in response to management practices and provide evidence-based support for carbon inventory. Since tree trunk diameter, more formally known as diameter at breast height (DBH), is correlated with tree biomass and associated carbon stocks, DBH is accepted as a means inferring the biomass–carbon stocks of trees. On ground measurement of DBH is straightforward but often time consuming and difficult in inaccessible terrain and certainly inefficient when seeking to infer stocks over large tracts of land. The aim of this research was to investigate various avenues of estimating DBH using synoptic remote sensing techniques. Tree parameters like crown projected area, tree height and crown diameter are all potentially related to DBH. This thesis first uses on–ground measurements to establish the fundamental allometric relationships between such parameters and DBH for scattered and clustered Eucalyptus trees on a large, ~3000-ha farm in north eastern part of New South Wales, Australia. The thesis then goes on to investigate a range of remote sensing techniques including very high spatial resolution (decicentimetre) airborne multispectral imagery and satellite imagery and LiDAR to estimate the related parameters. Overall, the research demonstrated the usefulness of remote sensing of tree parameters such as crown projection area and canopy volume as a means of inferring DBH on a large scale.