Lab

McMaster University Remote Sensing Laboratory

About the lab

Welcome to the Gonsamo Group, the Remote Sensing Laboratory of McMaster University. We focus on ground, airborne, and satellite remote sensing of vegetation from the leaf level to the global scale. In addition to remote sensing data, we use ground measurements of plant biophysical variables, photosynthetic traits, atmospheric CO2 concentration, eddy covariance CO2 fluxes and plant phenology; gridded climate data records; terrestrial ecosystem carbon cycle models; and Earth System Model (ESM) outputs.

Featured research (4)

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and-2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Senti-nel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage.
Canada has extensive forests and peatlands that play key roles in global carbon cycle. Canadian soils and peatlands are assumed to store approximately 20% of the world’s soil carbon stock. However, the spatial and vertical distributions of the soil organic carbon (SOC) concentration in Canada have not been systematically investigated. SOC concentration, expressed in g C kg⁻¹ soil, affects the chemical and physical properties of the soil, such as water infiltration ability, moisture holding capacity, nutrient availability, and the biological activity of microorganisms. In this study, we tested a three dimensional (3D) machine learning approach and 40 spatial predictors derived from 20 years of optical and microwave satellite observations to estimate the spatial and vertical distributions of SOC concentration in Canada in six depth intervals up to 1 m. A quantile regression forest method was used to build an uncertainty map showing 80% of prediction intervals. Results showed that a random forest model associated with 25 covariates was successful in capturing 83% of spatial and vertical SOC variation over the country. Soil depth was the most important covariate to predict SOC concentration, followed by surface temperature and elevation. The SOC concentration in forested areas was highest in the top layers (0–5 cm), but soils located in peatlands showed higher C concentration in all soil depths. Among the terrestrial ecozones of Canada, Pacific Maritime and the Hudson Plain mostly covered by forest trees and peatlands, respectively, show highest SOC concentration, while the lowest concentration are observed in the Prairies and Mixed Wood Plain ecosystems that are the agricultural areas of the country. This study provides a deeper understanding of the major factors controlling SOC concentration in Canada and shows potential areas with high carbon reserves, which are crucial in view of the ongoing climate change. In addition, the presented methodological framework has great potential to be used in future soil carbon storage inventories using satellite observations. Mapping SOC concentration and associated uncertainties in Canada are fundamental to detect trends of gains or losses in SOC linked to recent and future national or global policy decisions related to soil quality and carbon sequestration.

Lab head

Members (5)

Ricardo Barros Lourenço
  • McMaster University
Cheryl Anne Rogers
  • McMaster University
Xinran Gao
  • McMaster University
Chinyere Ottah
  • McMaster University
Max Salman
  • McMaster University

Alumni (2)

Rachel Badzioch
Rachel Badzioch