Brendan Cottrell is a MSc student in the Applied Remote Sensing Laboratory in McGill University's Geography department where he studies the application of remote sensing and drone technology in marine mammal conservation. Brendan graduated from Simon Fraser University with a Bachelor of Science in Applied Physics, later finishing an Advanced Certificate in Geographic Information Systems from the British Columbia Institute of Technology. After graduating from Simon Fraser University, Brendan worked with the British Columbia Marine Mammal Response Network where he was involved in marine mammal rescue, necropsies, and research. Brendan's current research is examining the ability of drones and remote sensing techniques to characterize and catalogue the prevalence and severity of entanglement scarring on large whales such as humpbacks. The collection of baseline data on large whale entanglement rates and impact allows for both fisheries and marine mammal managers to implement more effective solutions in limiting these events.

Digital elevation models (DEMs) were among the first digital geographical information to be readily available and widely used. At the Earth Resources Observation and Science (EROS) Centre of the United States Geological Survey (USGS), DEMs represent the most popular data set distributed with an average of one 1⁰ x 1⁰ tile downloaded every second of every day!

In archaeology, they are frequently employed, with examples dating back to the late 70s and early 80s, because they represent a concrete, directly observable phenomenon that humans can relate to: the surface of the planet.
DEMs allow a vast array of GIS-derived analyses (i.e. slope, shade, aspect, viewshed, path analysis, flood risk analysis, drainage modeling, etc) that all directly relate to how past and current humans, and their prey, experience, perceive, interact and adapt to the landscape and the environment. Topography increases biodiversity, influences soil development, human settlement, and land-use pattens. It is an important element in settlement patterns.

In Canada, Natural Resources Canada (NRCan) has produced the Canadian Digital Elevation Data (CDED) and the Canadian Digital Surface Model (CDSM). The NASA Jet Propulsion Laboratory (JPL), Japan’s Ministry of Economy, Trade and Industry (METI) and the Japan Aerospace Exploration Agency (JAXA) have also released global or near global DEMs (SRTM, ASTER GDEM, AW3D30 respectively). Each has its own qualities, limitations, and level of precision that may, or may not, be adapted to an archaeological research question. Faced with a multitude of DEMs to choose from, even researchers well versed in GIS find themselves lost in this myriad of numerical terrains.

My research goal is to help archaeologists and other researchers to find their way across the various digital landscapes. It focuses mostly on freely available models covering the Canadian landscape that overlap provincial boundaries while maintaining a Canadian perspective on the data as Canada’s northern latitudes present both advantages and unique challenges to model developers.

My PhD project looks at how DEMs are employed by archaeologists. My goal is to help researchers with important concepts that will help them understand the basic characteristics of current, and future elevation models. Using high precision (15 to 30 cm) light detection and ranging (LiDAR) data from 32 LiDAR campaigns flown across Canada (22,150 km²), each model is tested to assess its vertical precision as well as the usefulness of the metadata. This should permit archaeologists and others to carefully select the most appropriate DEM for their research question and will serve to answer Kvamme’s call for archaeologists to be concerned with “the quality of data obtained by computer means”.

Research interests: GIS, Python, DEM, LiDAR, HSI data fusion, Graph-Network analysis

Cadieux N., Kalacska M., Coomes O.T., Tanaka M., Takasaki Y. 2020 A Python Algorithm for Shortest-Path River Network Distance Calculations Considering River Flow Direction Data 5(1):8

Kathryn Elmer graduated with an MSc in Geography at McGill University as part of the Applied Remote Sensing Lab (ARSL), and received her BSc in Earth System Sciences from McGill University in 2017. She previously worked for the National Research Council of Canada’s Flight Research Lab (Ottawa, Ontario) and ARSL at McGill University (Montreal, Quebec), where she contributed to research on a variety of topics including the use of unmanned aerial vehicles (UAVs) as remote sensing platforms for ecological monitoring, satellite simulations, spatial accuracy of UAV platforms for 3D landscape reconstructions, and various field work campaigns. Her MSc thesis focused on using remote sensing technology, specifically hyperspectral imagery (HSI) acquired using airborne platforms for identifying and mapping the highly invasive plant species Phragmites australis subs. australis as part of CABO. Her field site was Îles-de-Boucherville National Park and she worked to provide the park management team with a park-wide Phragmites identification map. Her work aims to help establish remote sensing and HSI as an effective tool for the management of invasive species around the world.

Elmer K, Kalacska M, Arroyo-Mora J.P. 2021 Mapping the extent of invasive Phragmites australis subsp. australis from airborne hyperspectral imagery Frontiers in Environmental Science - Environmental Informatics and Remote Sensing 9:496

Elmer K., Kalacska M. 2021. A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada Data 6(3):32

Elmer K., Soffer R.J., Arroyo-Mora J.P., Kalacska M. 2020. ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data Data 5(4):96

Arroyo-Mora J.P., Kalacska M., Inamdar D., Soffer R., Lucanus O., Gorman J., Naprstek T., Schaaf E.S., Ifimov G., Elmer K., Leblanc G. 2018. Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring Drones 3(1):12

Kalacska M., Lucanus O., Arroyo-Mora J.P., Laliberté E., Elmer K., Leblanc G., Groves A. 2020. Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms Drones 4(2):13

Publications

Svob S., Arroyo-Mora J.P., Kalacska M. 2014 A wood density and aboveground biomass variability assessment using pre-felling inventory data in Costa Rica Carbon Balance and Management 9,9 https://doi.org/10.1186/s13021-014-0009-y

Svob S., Arroyo-Mora J.P., Kalacska M. 2014 The development of a forestry geodatabase for natural forest management plans in Costa Rica Forest Ecology and Management 327(1):240-250

Arroyo-Mora J.P., Svob S., Kalacska M., Chazdon R. 2014 Historical Patterns of Natural Forest Management in Costa Rica: The Good, the Bad and the Ugly Forests 5(7):1777-1797

Thesis: The creation of a forestry relational database and assessment of aboveground biomass variability across Costa Rica using forest management data

Publications

Kalacska M., Arroyo-Mora J.P., de Gea J., Snirer E., Herzog C., Moore T.R. 2013 Videographic Analysis of Eriophorum vaginatum Spatial Coverage in an Ombotrophic Bog Remote Sensing 5(12):6501-6512

Thesis: A study of the effects of cadaveric decomposition on hyperspectral signatures of soil and vegetation

Publications

Thesis: Detection of an experimental mass grave over time and at different spatial scales in a temperate environment

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Publications

Arroyo-Mora J.P., Kalacska M., Inamdar D., Soffer R., Lucanus O., Gorman J., Narstek T., Schaaf E.S., Ifimov G., Elmer K., Leblanc G. 2019. Implementation of a UAV–Hyperspectral Pushbroom Imager for Ecological Monitoring Drones 3(1),12

Arroyo-Mora J.P., Kalacska M., Soffer R., Ifimov G., Leblanc G., Schaaf E.S., Lucanus O. 2016. Evaluation of phenospectral dynamics with Sentinel-2A using a bottom-up approach in a northern ombrotrophic peatland Remote Sensing of Environment 216:544-560.

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Publications

Thesis: Hyperspectral Remote Sensing of Peatland Vegetation at Multiple Spatial Scales

Publications

Thesis: Estimating aboveground carbon stocks from remote sensed data and the TEAM network inventory data