Conference Agenda

This presentation examines the integration of Earth Observation (EO) data into Canada’s impact assessment (IA) processes, highlighting the progress and applications of the Earth Observation for Cumulative Effects – Phase 2 (EO4CE-2) program. Despite rapid growth in EO data acquisition, analytics, and delivery systems, uptake by IA practitioners has been limited due to persistent barriers such as awareness of EO capabilities, data accessibility, and analytical capacity. EO4CE-2, led by Natural Resources Canada’s Canada Centre for Mapping and Earth Observation (CCMEO), aims to address these challenges by providing high-quality, standardized EO datasets and operational frameworks to support transparent, data-driven IA processes.

EO4CE-2 has produced a library of EO-derived products leveraging decadal satellite records and advanced machine learning, enabling comprehensive analysis of land use, water resources, vegetation, lake and river ice, and terrain stability. These datasets allow decision-makers to evaluate environmental status and trends.

A key application is the Regional Assessment of the Ring of Fire (ROF) area in northern Ontario, where eleven EO-based indicators—covering water systems, wildlife habitat, forest ecosystems, permafrost, and terrain deformation—support assessment priorities such as environmental health, social equity, and community well-being. Indigenous communities have played a central role in validating these indicators and contextualizing EO data. The results demonstrate that combining satellite observations with local knowledge enhances regional assessments, supports sustainable resource management, and informs evidence-based decision-making. This presentation highlights EO4CE-2’s achievements, challenges, and lessons learned in advancing the use of EO for cumulative effects assessment in Canada.

Forest biomass plays a central role in carbon accounting, climate modeling, and sustainable forest management. However, large-scale biomass estimation remains challenging due to the limited spatial coverage of field inventories and the inherent spectral saturation issues of optical remote sensing in dense forest canopies. This study presents an operational workflow for mapping above-ground biomass (AGB) across southern Québec using multi-source Earth observation data and machine learning implemented in Google Earth Engine. The approach integrates Sentinel-2 optical composites, Sentinel-1 dual-polarization SAR metrics, and a high-resolution 1-m canopy height model with detailed plot-level biomass derived from Québec’s Placettes-Échantillons Permanentes (PEP) network. A Gradient Tree Boosting model was trained on 4,083 quality-controlled field plots to capture species, structural, and spectral variability. Validation results show strong agreement between predicted and observed biomass (R² ≈ 0.76, RMSE ≈ 14.4 Mg ha⁻¹), demonstrating the value of fusing optical, radar, and structural predictors. The resulting biomass and carbon maps provide actionable information for forest monitoring, regional reporting, and environmental decision-making. This contribution highlights the effectiveness of cloud-based multi-sensor fusion for operational AGB estimation and offers a scalable methodology applicable to broader Canadian forest regions.

Seasonal snow remains a critical component of Canada’s water cycle, yet consistent, high-resolution monitoring of snow water equivalent (SWE) is still lacking at national and hemispheric scales. The Terrestrial Snow Mass Mission (TSMM) proposes a dedicated dual-frequency Ku-band radar satellite designed to deliver spatially continuous SWE estimates at 500 m resolution with a 5–7 day revisit rate. To prepare the scientific foundations of this mission, the TSMM Academic Consortium has expanded to 16 Canadian institutions and now integrates data from over 40 long-term snow research sites.

Between 2024 and 2026, the consortium conducted ten coordinated field campaigns across mountainous and Arctic environments, in collaboration with Environment and Climate Change Canada, the Canadian Space Agency, and the European Space Agency. These campaigns combined ground-based and airborne Ku-band radar, detailed snowpit measurements, microstructure characterization, UAV surveys, and GNSS mapping. Joint ESA–TSMM activities at Cambridge Bay further enhanced Ku-band validation in deep Arctic snow.

Recent advances include improved dual-frequency Ku-band inversion methods, refined radiative transfer models, enhanced wet/dry snow classification, and integration of radar-derived SWE into snow model simulations and CaLDAS assimilation frameworks. Together, these developments confirm TSMM’s feasibility and scientific readiness.

This contribution summarizes the consortium’s field results and retrieval advances, demonstrating the mission’s potential to provide operational SWE monitoring essential for hydrology, climate science, wildfire preparedness, and Arctic environmental security.

The disaster resilience in urban environments remains a critical yet often underexplored component of sustainable development, particularly in densely populated regions where schools and community shelters serve as vital emergency infrastructure. Despite their importance, the systematic assessment of these shelters’ suitability is frequently overlooked, leading to disparities in safety, accessibility, and preparedness during crisis events. This research introduces a comprehensive, data-driven framework for evaluating the suitability of educational institutions and community shelters using Gaussian Process Regression (GPR). The proposed model integrates multiple geospatial and infrastructural parameters, including environmental risk exposure, proximity to fault lines and water bodies, structural integrity, road connectivity, and population density. By modeling the complex nonlinear relationships among these significant factors, the Gaussian Process Regression (GPR)-based approach predicts shelter safety scores that reflect the relative resilience and accessibility of each location. The predicted scores are spatially visualized using interactive geospatial mapping tools, allowing decision makers to easily identify safer zones or shelters and high-risk clusters across Delhi. The areas with higher scores correspond to shelters with strong infrastructure and better access to emergency resources and open spaces, whereas lower-scoring regions indicate vulnerable areas in need of immediate policy attention and structural reinforcement. The outlier detection techniques further enhance the interpretability of results by identifying anomalous schools with unusually high or low suitability for deeper investigation. The model’s performance, evaluated through five-fold cross-validation, reveals variability in Mean Squared Error (MSE) across folds, indicating sensitivity to spatial heterogeneity and highlighting potential improvements through hyperparameter tuning and ensemble learning strategies.

Rapid urbanization has profoundly reshaped the spatial dynamics of population distribution, environmental quality, and resource utilization, particularly in megacities such as Beijing. As population density, industrial activity, and transportation intensity continue to rise, air pollution has become a key constraint on sustainable resource development and urban livability. In recent years, the integration of Earth Observation (EO) with geospatial analytics has provided new opportunities for monitoring, modeling, and managing urban environmental systems.

For instance, Liu et al. employed complex network theory to analyze regional air quality variations in the Yangtze River Delta[1], while Rabie et al. developed a CNN–Bi-LSTM hybrid framework to predict spatially resolved AQI in megacities[2]. Similarly, Similarly, Ma et al. used a temporal-encoded Informer model to forecast AQI in northern China[3],and Ahmed et al. demonstrated that EO-derived hydro-climatological variables can substantially enhance AQI prediction accuracy when combined with deep learning models[4].Moreover, Sarkar et al. proposed an effective hybrid deep learning model for AQI prediction, which further validates the potential of hybrid approaches in capturing complex urban air pollution patterns[5].However, most existing studies emphasize temporal forecasting or algorithmic improvement, while the spatial interaction between population distribution and air quality remains insufficiently explored. To bridge this gap, this study develops an EO-supported geospatial framework that integrates demographic and environmental data to analyze spatial heterogeneity and exposure inequality in Beijing, providing data-driven insights for sustainable resource and environmental governance.

In this study, we compared the performance of four machine learning (ML) models, including Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and Support Vector Regression (SVR), for predicting and mapping key water quality parameters, namely dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and turbidity in Fanshawe Lake using two distinct satellite datasets: the high-resolution PlanetScope and the Sentinel-2 imagery.

Eleven commonly used spectral indices sensitive to suspended particles and algae were derived from PS and Sentinel-2 imagery, combined with in situ measurements collected from 2018 and 2024 to train and validate the models. We evaluated the ML models using R², mean absolute error (MAE), and root mean squared error (RMSE). Our study shows that using machine learning with satellite imagery can provide encouraging predictions of key water quality indicators in Fanshawe Lake. There are certain benefits of using high spatial and temporal resolution PS satellite imagery instead of Sentinel-2 datasets to capture localized changes in water quality parameters. The Upper Thames River Conservation Authority can use these results to predict when algal blooms might occur in Fanshawe Lake. Future research may investigate the capture of seasonal trends through the integration of additional field and satellite datasets with time-series models, such as Long Short-Term Memory.

Climate change is rapidly affecting glaciers and seasonal snow in high-altitude regions, which in turn threatens water resources and mountain ecosystems. In this study, I aim to understand and quantify these climate-driven changes by combining data from Sentinel-1 radar and Sentinel-2 optical satellite imagery. By analyzing datasets collected over multiple years, I can observe how glaciers are retreating, snow cover is changing, snow grain size is evolving, and seasonal melt patterns are shifting. To achieve this, I use a combination of advanced spectral and radar indices along with machine learning techniques to extract detailed information about snow and glacier characteristics and track their changes over time. These results allow me to evaluate when snow melts and how it may affect downstream water flow, which is essential for sustainable water management and maintaining ecosystem health. I also make use of cloud-based platforms like Google Earth Engine to efficiently process large volumes of satellite data. By integrating AI-driven analysis with remote sensing, I can produce accurate, large-scale maps and insights that help predict future trends. The outcomes of my study are not only important for understanding how climate change is impacting glaciers and snow in my study area but also provide a framework that can be applied to other mountain regions around the world. Ultimately, my research offers valuable information for planning climate adaptation strategies and ensuring the resilience of both water resources and mountain ecosystems.