Lakehead Applied Geomatics Research Laboratory (LAGRL)

The Lakehead Applied Geomatics Research Lab (LAGRL), under the direction of Dr. Muditha Heenkenda started working extensively at the Lakehead University Agricultural Research Station (LUARS) in 2021.

Since then multiple projects employing Drones, stationary multi-spectral cameras, and a new robot for monitoring crops as they grow, have been undertaken together with undergraduate and Master’s students in partnership with the staff at LUARS.

A DJI Matrice 350 drone hovering about 1.5 meters above a black carpet landing pad which is placed on green grass with agricultural crops surrounding it. In the background are the Nor'wester Mountains against a sky made hazy from wildfire smoke.

Drone image looking down at person attaching wires to a stationary camera setup including a battery, solar panel and white PVC rig with two cameras

The beginning of a prototype robot constructed from 2 old bicycles and aluminum strut. A pair of Mapir cameras hang from a jib, there is a solar panel leaning against the wall which has a large wall map depicting the industrial resources of the former Soviet Union.

Recent Research

Digital Twin / Facility Management

A recent Masters student, Nilanika Goonetilleke, created a digital twin of the Regional Centre on the Thunder Bay campus of Lakehead University. Using ArcGIS Indoors, the building was modelled to provide indoor navigation, room booking, work order generation for maintenance, and other aspects of facility management. Nilanika created an informative ArcGIS StoryMap to display the results of this research.

Estimating Tree Diameter with Lidar

Multiple studies refining an approach to estimating tree diameter at breast height (DBH), using the lidar available on the iPad Pro (Fangyi Wang, et al. 2022; Guenther, et al. 2024). Consumer level tools like the iPad Pro show promise with careful project design and point-cloud processing.

Ag-Bot for crop monitoring

In an effort to gain higher spatial resolution, including more detailed 3D modelling of plant morphology, a robot is currently being designed for testing during summer 2026. Flying a drone at lower altitudes necessary for high spatial resolution results in prop-wash that degrades the images. The Ag-Bot will have multiple multi-spectral cameras to create orthophotomosaics and 3D models of row crops. Most previous studies have focused on spectral signatures as a way to measure crop health. This will be combined with information about how quickly leaves are growing, and crop canopy and crop height is increasing.

Aerial image of an agricultural field with small experimental plots displayed in false colour to include a near infra red band.

The Future of Earth from Satellites

Spring of 2026 marked the inaugural offering of the The Future of Earth from Satellites microcredential course through the Lakehead University Community Zone with support from the Canadian Space Agency. This ten day course explores the use of satellite imagery for Earth Observation and is intended for students and career professionals who have little previous experience with GIS or Remote Sensing.

LAGRL Geomatics Network

A Geomatics Network has been established to support Earth Observation, GIS and remote sensing practitioners and to communicate best practices in the field. Dr. Muditha Heenkenda, along with technician Reg Nelson will moderate a new Zulip instance intended to foster conversation and provide a platform for mutual assistance with remote sensing, GIS and associated technologies. Successful participants of The Future of Earth from Satellites will be invited to join this group along with other knowledgeable researchers and practitioners. Reach out to gdc@lakeheadu.ca if you want to participate and join the conversation.

Oblique drone photograph of the Lakehead University Agricultural research station. Plots of Winter Wheat are in the foreground, with the drone pilot at the edge of the field. Green fields stretch into the distance with Nor'wester mountains in the background.

LAGRL Publications

Fallas Calderón, I. D. l. Á., Heenkenda, M. K., Sahota, T. S., & Serrano, L. S. (2025). Canola Yield Estimation Using Remotely Sensed Images and M5P Model Tree Algorithm. Remote Sensing, 17(13), 2127. https://doi.org/10.3390/rs17132127

Guenther, M.; Heenkenda, M.K.; Leblon, B.; Morris, D.; Freeburn, J. (2024) Estimating Tree Diameter at Breast Height (DBH) Using iPad Pro LiDAR Sensor in Boreal Forests, Canadian Journal of Remote Sensing, 50:1, DOI: 10.1080/07038992.2023.2295470

Guenther, M.; Heenkenda, M.K.; Morris, D.; Leblon, B.(2024) Tree Diameter at Breast Height (DBH) Estimation Using an iPad Pro LiDAR Scanner: A Case Study in Boreal Forests, Ontario, Canada. Forests, 15, 214. https://doi.org/10.3390/f15010214

Fuentes, M.V.B.; Heenkenda, M.K.; Sahota, T.S.; Serrano, L.S. (2024) Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada. Crops, 4, 115-133. https://doi.org/10.3390/crops4020009

Vásquez, R. A. R., Heenkenda, M. K., Nelson, R., & Segura Serrano, L., (2023). Developing a New Vegetation Index Using Cyan, Orange, and Near Infrared Bands to Analyze Soybean Growth Dynamics. Remote Sensing, vol. 15(11), 2888. MDPI AG. http://dx.doi.org/10.3390/rs15112888.

Carrillo, V. C. Heenkenda, M. K., Nelson, R., Sahota, T. S., Segura Serrano, L., (2022). Deep learning in land-use classification and geostatistics in soil pH mapping: a case study at Lakehead University Agricultural Research Station, Thunder Bay, Ontario, Canada, Journal of Applied Remote Sensing vol. 16(3), 034519. https://doi.org/10.1117/1.JRS.16.034519.

Fangyi Wang, Heenkenda, M.K., & Freeburn, J.T., (2022). Estimating tree Diameter at Breast Height (DBH) using an iPad Pro LiDAR sensor, Remote Sensing Letters, vol. 13(6), 568-578, DOI: 10.1080/2150704X.2022.2051635.