Remote Sensing Team (McDermid Lab)

Our scientific goal is to develop and operationally apply innovative theoretical and methodological approaches to better understand, map, monitor and model the multiscale dynamics of ecosystem patterns and processes. We conduct pure and applied research in remote sensing, wildlife ecology, urban studies, and natural resource management.

Dr. Greg McDermid
Associate Professor

University of Calgary

I am a geospatial scientist whose research activities revolve around the application of remote sensing and other geospatial technolgies to environmental monitoring and ecology. I am involved in a wide variety of interdisciplinary research collaborations in the fields of wildlife ecology, biodiversity assessment, ecological monitoring, plant-phenology assessment, and vegetation mapping. I’m interested in a broad range of remote sensing and other GIScience pursuits, including digital image processing, spatial analysis, environmental modelling, and data fusion/integration.

Team Members

Dr. Mir Mustafizur Rahman
Research Technician

University of Calgary

I am a Geospatial Scientist, developing innovative geospatial solutions for understanding and monitoring the physical environment at different scales, whether within multi-faced and diverse urban settings or broader natural settings. Consequently, I am involved in a variety of multidisciplinary research projects in the field of ecological modeling, wetland and vegetation mapping, grassland mapping and plant phenology analysis, and Urban Planning and Urban Energy Budget. The ultimate goal of my research works is to continue working towards a flexible and holistic approach to the use of geospatial tools for large-area resource management. My current role in BERA as a lead remote sensing scientists involve supervising geospatial research activities within the scope of the project.

Shannon Blackadder
Research Technician

University of Calgary

Man Fai Wu
Ph.D. Student

University of Calgary

Corey Feduck
MSc Student

University of Calgary

UAV Workflows for Assessment of Vegetation Structure

I am an MGIS student and Geospatial Technician for the BERA project and responsible for operating our fleet of unmanned aerial vehicles (UAVs) for the Remote Sensing Team at the University of Calgary. This summer I will be collecting aerial data for BERA and managing sensor integration, platform modifications and contributing to best practices for in field data collection. I have been providing support for several projects focused on vegetation analysis, but my personal research focus is to develop an automated workflow for rapid assessment of small replanted coniferous seedlings (<10 cm). UAVs are likely to decrease the need for onsite manual surveys from personnel for a variety of resource industries in Canada. The objective of my research is to create a scale invariant, sample-based algorithm that is stable across different study areas. Results of classified pine density estimates are accurate enough to suggest that UAVs can soon be used in an operational role for monitoring silviculture activities. My research also aims to prove that sample based flight plans are an effective method of extending UAV flight range for vegetation assessment tasks.

Sarah Cole
MSc Student

University of Calgary

The Development of Remote Sensing Tools for Mapping Linear Disturbance Features

The primary goal of my research is to develop remote sensing tools and protocols for accurately delineating the location and dimensions of linear disturbance features within a northern boreal environment in a more cost-efficient manner. To achieve this, airborne light detection and ranging data was used to extract linear features using a least-cost path analysis based on the relative height of vegetation canopies derived from digital elevation models and digital surface models. The results were compared to reference data collected in the field, which included unmanned aerial vehicle-collected imagery that was used to create point clouds for the extraction of comparable metrics (i.e., width of the line). The tools developed here will enhance our capacity to map human linear disturbances, and support ongoing efforts to understand the environmental effects of resource extraction in Canada’s boreal regions.

Gustavo Lopes Queiroz
MSc Student

University of Calgary

Remote Sensing of Coarse Woody Debris for Caribou Habitat Restoration in Alberta’s Boreal Forest

The focus of my research is the development of an automated technique for Coarse Woody Debris (CWD) detection and measurement in seismic lines of Alberta’s Boreal Forest. The presence and density of CWD in seismic lines are important aspects of the environmental restoration framework set by the Government of Alberta to promote caribou habitat restoration. I will test different feature-extraction algorithms: machine-learning based classification versus more traditional pattern recognition. The results from different methods will be compared against ground-truth data for CWD collected at the test sites. By analyzing the results of this study, I will select a favoured method and adapt it to best fit the theoretical and practical challenges posed by this specific environment.

Christina Braybrook
BSc Student

University of Calgary

Photogrammetric point clouds from terrestrial and aerial imagery for forest mensuration

Traditional forest mensuration or measurement relies on detailed field sampling procedures that involve both direct tree measurements, such as diameter and height, and visual estimations of stand characteristics, such as canopy cover or crown closure. The latter can be subject to strong user bias, experience or error and detailed field protocols are time-consuming. Recent advances in computer technology and computer vision-based software, however, present an opportunity for a new approach to forest mensuration. Series of overlapping digital photographs taken of an object or surface from a variety of angles can be used in a computerized Structure-from-Motion workflow to generate dense, three-dimensional models of that object or surface, in the form of a point cloud. This point cloud is similar to what is produced by a LiDAR instrument, and such photogrammetric point clouds, built from overlapping aerial photographs, have already proven useful in forest characterization. Terrestrial digital photographs, however, present yet another source of imagery for point cloud generation and could be merged with aerially-based point clouds. This work examines best practices and optimal procedures for collecting digital imagery of a forest vegetation plot, both terrestrially and aerially, in support of point cloud-based forest mensuration analysis.