Students

PhD Candidate

The overarching goal of my research is to understand how bird species occupy the forest across a structural gradient. My project uses airborne laser scanning (ALS) data, supported by forest mensuration and songbird presence/absence data collected at the Queen's University Biological Station (and Monk’s Wood, Cambridgeshire, UK). 

My specific objectives are to:

1. Identify ALS-derived structural metrics relevant to Cerulean warbler, a species of conservation interest
2. Examine the relationship between Cerulean warbler and species richness and occurrence of bird species with overlapping niches; and
3. Assess the ability of spectral data (e.g., Landsat) in combination with ALS data to explain forest structure and predict Cerulean warbler occurrence.

PhD Candidate, co-supervised by Dr. Ryan Danby

In response to accelerated warming trends across northern latitudes, NASA’s Arctic-Boreal Vulnerability Experiment (ABoVE) was established. The ABoVE campaign has collected various types of remote sensing (RS) data (i.e., airborne hyperspectral imagery, LiDAR, and SAR) across Alaska and Western Canada since 2015.

Using data from 2017 to 2019, my research will combine unique qualities from these datasets to assess spatial patterns of a forest-tundra transect in northwest Canada. The outcomes of this research will contribute to our knowledge base on how the synergistic use of different types of RS data can improve forest-tundra monitoring, with potential contributions to advancing climate modelling capabilities and forest management practices.