STUDENT OPPORTUNITIES
Contact Dr. Paul Treitz for information about graduate and undergraduate student research opportunities.
CURRENT GRADUATE STUDENTS
David Atkinson (PhD Candidate)
I am developing methods for estimating biophysical variables of arctic vegetation communities based on remotely sensed data. The objectives of my research are to: 1) quantify biomass, biodiversity, and species abundance for vegetation communities at various latitudes; and 2) relate field-based measures to satellite derived estimates of biomass over the landscapes. My study sites are Boothia Peninsula, Nunavut and Cape Bounty, Melville Island, Nunavut.
Karin van Ewijk (PhD Candidate, co-supervised by Dr. Neal Scott)
My research aims to quantify 3D forest structure complexity, using LiDAR and high spatial resolution imagery, in order to identify and understand successional stages in forest ecosystems. Subsequently I hope to predict successional pathways for these ecosystems using a modeling framework. A second objective is to link the observed successional stages to forest productivity and predict how these stages and their productivity may change over time. My study site is the Petawawa Research Forest which is located within Central Ontario’s Great Lakes – St. Lawrence Forest Region.
Neal Pilger (PhD Candidate)
My research involves the coupling of variable density LiDAR and high-resolution multispectral remote sensing data to estimate leaf area index (LAI), biomass, and aboveground carbon in mixed-wood Ontario forests. These estimates will be then applied to a modified ecological process model for the estimation of net primary productivity (NPP). The LiDAR-fed process-modeling framework will be used to make predictions on the future state of Ontario’s Great Lakes / St. Lawrence forests in the sequestration of carbon under different environmental scenarios.
Fiona Gregory (MSc Candidate, co-supervised by Dr. Neal Scott)
I am studying the relationships between carbon flux, spectral response, soil characteristics, and vegetation community type. My focus is on how these factors change throughout the growing season, and how information gained from field observations can be scaled up using multitemporal high resolution remotely sensed images. My study site is Cape Bounty, Melville Island, Nunavut.
Adam Collingwood (PhD Candidate)
The objectives of my research are to determine the influence of multi-polarimetric RADARSAT-2 SAR data on soil moisture and vegetation analysis; to better understand and separate the interacting effects of soil moisture, vegetation, and topography on SAR backscatter values; and to determine soil moisture and vegetation distribution across an Arctic watershed. My study area is Cape Bounty, Melville Island, Nunavut.
Graham Pope (MSc Candidate)
My research examines Biomass and Net Primary Productivity in Boreal forests near Hearst, Ontario. A vegetation property called Leaf Area Index (LAI) will be estimated using remote sensing and terrestrial techniques. Aerial LiDAR (Light Detection and Ranging) will be the primary method of estimating LAI, with ground-based Canopy Hemispherical Photographs used for validation. The goal of this research is to verify the accuracy of the low cost, LiDAR method. Successful validation will aid in both ecological health observation and commercial forestry assessment.
Sarah Allux (MSc Candidate)
My research aims to characterize spatial variability in vegetation community composition, percent cover, and leaf area index (LAI) on the Sabine Peninsula, Melville Island, Nunavut using Worldview-2 satellite imagery. I am investigating how NDVI and other spectral vegetation indices relate to these vegetation characteristics at multiple spatial scales, focusing on developing and testing new indices for High Arctic vegetation. I am also interested in evaluating various statistical methods of upscaling relationships between vegetation spectral response and biophysical variables.
CURRENT UNDERGRADUATE STUDENTS
Sadie Eastwood (BSc Honours, 2011 - 12, co-supervised by Dr. Neal Scott)
The focus of my research is to examine the phenological phases of a forest located the Queen's University Biology Station (QUBS). Phenology is an important topic to study because it is useful in examining the relationship between climatic factors such as temperature, precipitation, amount of solar radiation etc and the life cycle of a plant. The green-up and senescence of individual tree species will be determined using an extensive time series of photographs collected above a forest canopy from (QUBS). I will examine the species specific responses and determine if their phenological phases are changing over time. I will also examine the relationship between these phenological phases and the corresponding climatic factors over a three year time period. A supervised classification of the field site using an IKNONOS image will be created and used to infer phenological properties of the different forest types covering QUBS.
LARSEES WALL OF FAME (ALUMNI)
Alison Cassidy, MSc (2011): The Effects of Recent and Relict Permafrost Disturbances on Tundra Vegetation, Cape Bounty, Melville Island, Nunavut.
Greg McQuat, MSc (2011): Feature Extraction Workflows for Urban Mobile-Terrestrial LIDAR Data.
Meg Southee, MSc (2010): Ecological Land Classification and Soil Moisture Modelling in the Boreal Forest using LIDAR Remote Sensing.
Stephanie Gagliardi, BScH (2010): Ecosite Classification and Forest Productivity: An Analysis of the Relations between Canopy Structure and Ecosite Class.
Anne Hagerman, BScH (2009): Estimating Basal Area in Tolerant Hardwood Stands using LIDAR: An Investigation of Field Basal Area Census Methods.
Laura Chasmer, PhD (2008): Canopy Structural and Meteorological Influences on CO2 Exchange for MODIS Product Validation in a Boreal Jack Pine Chronosequence.
Holly Shulman, MA (2008): Estimating Evacuation Vulnerability of Urban Transportation Systems using GIS.
Melissa Fedrigo, BScH (2008): Comparison of Digital Elevation Data derived from Topographic Maps and Airborne LIDAR Acquisition under Varying Forest Canopy Densities.
Nick Gralewicz, BScH (2008): LIDAR Estimation of Biophysical Variables in Pristine Northern Tolerant Hardwood Stands.
Kimberly Fairholm, BScH (2007) : Mid Arctic Vegetation: Community Structure Effects on Soil Carbon, Nitrogen, and Water.
Andrew Farrar, BScH (2007): A Comparison of Wetland Sub-Classification Accuracy using IKONOS-2 and LANDSAT-5 Satellite Imagery: A Case Study of Bastard Township, Ontario.
Kevin Lim, PhD (2006): LIDAR Remote Sensing of Forest Canopy and Stand Structure
Valerie Thomas, PhD (2006): Spatially Explicit Modelling of Forest Structure and Function using Airborne LIDAR and Hyperspectral Remote Sensing Data combined with Micrometeorological Measurements
Shanley Thompson, BScH (2006): Soil Moisture and Vegetation Patterns on Boothia Peninsula
Björn Prenzel, MSc (2005): Remote Sensing and GIS for Thematic Land Surface Analysis and Monitoring: A Case Study of the Tondano Study Area, Sulawesi, Indonesia
Margot Hessing-Lewis, MSc (2005): Assessing the Potential for Eelgrass Restoration in the Squamish Estuary, British Columbia
Jake Wall, MSc (2005): Arctic Remote Sensing of Soil Moisture with Multitemporal SAR Imagery
Andrew Maher, MSc (2005): Assessing Snow Cover and its relationship to Distribution of Peary Caribou in the High Arctic
Alexandra Taylor, MA (2005): Inuit Qaujimajatuqangit about Population Changes and Ecology of Peary Caribou and Muskoxen on the High Arctic Islands of Nunavut
Freyja Forsyth, BScH (2005): Soil Moisture and Arctic Vegetation Community Structure
Gita Laidler, MSc (2002): Multi-Resolution Remote Sensing Data for Characterizing Tundra Vegetation Communities on Boothia Peninsula, Nunavut
Craig Sheriff, BScH (2002): Soil Moisture Estimation of Arctic Soils using SAR
Peter Andrew-McBride, BScH (2002): The Effects of RadarSat Incidence Angle on Agricultural Crop Statistics
Valerie Thomas , MSc (2001): Hyperspectral Assessment of Acer saccharum Forest Structure
Paul Sampson, MSc (2000): Forest Condition Assessment: An Examination of Scale, Structure, and Function using High Spatial Resolution Remote Sensing Data