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General Research Interests

My interests in research are:

The application of remote sensing techniques to better understand environmental processes.

The increase in knowledge of complex boundaries such as vegetation as an input into 1D/2D/3D
hydraulic modeling, and into ecosystem demography models.

The application of physically based scientific knowledge to the improvement of Hydrological Ecozones that have been unsustainably perturbed by anthropogenic factors and to better monitoring of change in regional and global ecosystems.

 Current Research Project

There are uncertainties in regional and global terrestrial carbon budgets due to the current state heterogeneity of forest ecosystems, the dynamics of carbon storage, and the changes in forest ecosystems resulting from disturbance and recovery processes. Therefore, consistent measurements of canopy structure and forest attributes such as canopy height, vegetation age, trunk diameter and height, canopy gap, aboveground biomass, and species identification amongst others, may help in providing information regarding the current state forest structure that is vital for assessments of current and future forest sustainability, biodiversity, and terrestrial carbon budgets.
Lidar and radar remote sensing techniques are capable of these measurements, with full waveform lidar measurements at near-infrared pulse emissions being sensitive to the vertical vegetation profile, and radar measurements a P, L, and C-bands sensitive to forest volume and density.

Lidar and radar remote sensing techniques on their own right are powerful tools in determining forest structure. In this project they will be investigated separately and also fused at the signal and parameter level to extract 3D forest structure and biomass at a variety of distinct ecosystems. The uncertainties in determining these parameters at different scales and spatial heterogeneity will be quantified by simulating regional carbon fluxes and performing sensitivity analyses using the Ecosystem Demography (ED) model. An end goal of this research is to assimilate lidar and radar remote sensing measurements of vegetation structure into the ED biosphere model in order to improve predictions of log-term vegetation responses to climate change. With this information the two active remote sensing techniques will be considered for global coverage on a spaceborne mission concept.

Doctoral Research

My phd work focused on the need to better parameterise the roughness of vegetation when considering and modelling inundation flows. This arose from the need to understand the flood risk implications of restoring woody vegetation to the immediate riparian zone and to the floodplain. LiDAR and multispectral data such as CASI have proven to be a very important new data source to physically characterise floodplain vegetation. Typically LiDAR data would be interpolated into Terrain Models or bare earth conditions using just the last pulse LiDAR data. To consider the vegetation and tree canopies, the first pulse reception needs to be manipulated.

Resistance formulas based on Leaf Area Index, trunk-stem morphology, vegetation spacing, and derived allometric relationships can be parameterized directly from scan models of tree stands on an individual basis. This can be either at the scale of rigid stems, to complex canopy structures. Thus there is the need to consider the relevant scale of remote sensing technique to best describe the differences in vegetation complexity.

One strategy of extracting roughness parameters from these remote sensing techniques is to use a data fusion object classification model. This involves extracting trunk and tree type information from Airborne LiDAR and CASI. Further strategies of extracting roughness parameters lie with the idea that trees are more than stems, and in larger rivers flow can rise into the canopy. Terrestrial Laser Scanning can be used to define complex structures at a millimetre scanning resolution for the purpose of extracting canopy parameters relevant for the parameterisation of the resistance equations. Thus in terms of modelling, what are the appropriate length scales of aggregation of landscape units, and does this spatial pattern influence model sensitivity in the 1D, 2D, and 3D?