Conference Agenda

Wetland characteristics such as size, inundation permanence and timing, and surface hydrological connectivity substantially impact wetland processes and functions. The ability to monitor these types of wetland characteristics, and changes through time, is dependent on the spatial and temporal resolution of the imagery data used to map wetland locations. Existing inventories of surface water features have largely been limited to permanently open water features such as lakes and ponds larger than 1km2 at monthly or annual intervals. To address these limitations a random forest model was trained to predict sub-pixel water fraction (SWF) in Sentinel-2 imagery at 10m and 20m spatial resolution. This approach facilitated the detection of small surface water features, including water features interspersed with vegetation such as wetlands, at a sub-monthly temporal scale. Overall, in the 10m SWF data, small and narrow water features were detected that were not apparent at the 20m scale, the shape of feature boundaries was more precise, and the continuity of narrow channels was better maintained compared to the 20m SWF data. Improved detection of small features and narrow channels supports improved wetland inventories, particularly regarding the inclusion of small wetlands which are important biogeochemical hotspots, and automated surface water connectivity classification. The temporal resolution of Sentinel-2 facilitates the detection of ephemeral inundation and wetland surface hydrologic connections, as well as monitoring changes in inundation and connectivity through time.

Salt marshes are ecologically significant ecosystems increasingly threatened by sea level rise, climate change, sediment disruption, and human pressure. Accurate delineation of marsh boundaries is essential for monitoring spatial and temporal change and informing conservation strategies. Remote sensing imagery provides an efficient means to map these boundaries over large areas. This study used high-resolution WorldView-3 imagery (0.3 m after pan-sharpening) to evaluate two methodological categories for mapping marsh extent in the Fraser River Delta, Canada: index-based thresholding (Global Otsu and Adaptive Otsu) and machine learning classification (Random Forest, K Nearest Neighbors, and Support Vector Machine). Each method produced binary marsh maps that were converted to boundary vectors and validated against field-surveyed marsh edges using spatial accuracy metrics, including mean distance error and RMSE. Adaptive Otsu achieved the highest accuracy (mean distance 0.42 m; RMSE 0.53 m) and effectively delineated boundaries across contrasting marsh conditions. Global Otsu performed moderately (mean distance 0.47 m; RMSE 0.62 m). Machine learning models showed lower accuracy overall: Random Forest (0.56 m; 0.73 m), K Nearest Neighbors (0.57 m; 0.76 m), and Support Vector Machine (0.71 m; 0.90 m). These findings demonstrate that locally adaptive thresholding outperforms traditional thresholding and machine learning classifiers for fine-scale marsh boundary extraction in heterogeneous coastal environments, offering a practical approach for remote sensing-based marsh monitoring.

Multispectral drone sensors enable fine-scale ecological mapping, but added bands can inflate processing costs. We evaluated the MicaSense RedEdge-MX Red and Blue cameras (5 bands each) versus the Dual Camera System (10 bands) for vegetation mapping in two salt marsh sites in Aulac, New Brunswick, Canada (24 classes at the reference site; 15 at the restoration site). Pixel-based Random Forest (RF) classifications were used to compare validation accuracy, variable importance, and processing time for stitching and classification. Five-band maps achieved up to 95% validation accuracy; the 10-band configuration improved accuracy by ≤2%. Band contributions were site dependent: the near-infrared (NIR) band from the Red camera aided classification at the reference site, whereas additional red-edge bands in the Blue/Dual setups improved performance at the restoration site. However, stitching time rose sharply for the Blue and Dual systems, and RF classification time scaled with data volume and class complexity. Overall, the 5-band Red camera provided a cost-effective balance of accuracy and efficiency, offering practical guidance for sensor selection in drone-based salt marsh monitoring.

Protection of wetlands in Canada is becoming increasingly important as the ecological services they provide become more well understood and simultaneously, as the advance of human settlement and impacts of climate change imperil them. Rapid and effective identification of wetland areas is crucial for this protection. While there is an estimated 1.2 million km2 of wetland area across the country, only a very small portion of this area is currently mapped and classified in accordance with the 5 major classes and 9 subclasses of the Canadian National Wetland Inventory (CNWI). Additionally, the mapping that has already been completed in some areas is of limited accuracy. To increase accuracy and reduce the cost of wetland mapping we use a combination of Sentinel-1 SAR and Sentinel-2 Multispectral images with topographical data (an SAR-derived DEM). Seasonal variations in water level and vegetation were accounted for through the acquisition of imagery from both satellites in May, July, and September. Using the Montérégie region of southern Quebec as a case study we use a combination of the images and DEM metrics for the entire study area to classify landcover into 21 classes with the Random Forest classifier. The initial Random Forest classification produced an overall classification accuracy of 96.3%. Our study shows that classifying Sentinel-1 and 2 images allows us to determine the location and type of wetlands with a high degree of accuracy. This will allow for more efficient conservation strategies in the mapped areas.

Coastal marshes provide critical ecosystem services, including habitat for diverse plant, fish, and bird communities, shoreline protection, and carbon storage. These low-lying ecosystems are increasingly threatened by sea-level rise and human pressures, necessitating systematic monitoring to inform conservation and restoration efforts. Marsh vegetation characteristics, such as species composition and leaf area index (LAI), are key indicators of ecosystem condition, yet traditional field surveys are often labor-intensive, costly, and spatially limited. High-resolution remote sensing offers a powerful alternative, providing extensive spatial coverage and repeated observations for long-term monitoring. In this study, 30 cm WorldView-3 imagery of the Sturgeon Bank Wildlife Management Area in southern British Columbia, Canada, was combined with machine learning (Random Forest) and deep learning models (2D CNN and Vision Transformer, ViT) to map marsh vegetation species and estimate LAI. Extensive field surveys were conducted at selected sampling points along 24 transects to document species composition and measure LAI, which datasets were used for model training and validation. Results show that the ViT model achieved the highest classification performance (Overall Accuracy 94.05%, Kappa 93.44%), outperforming CNN and RF, and was used to generate a species distribution map. Random Forest, while less effective for classification, accurately estimated LAI (R² ~0.85), producing an LAI map that, combined with the species map, revealed species-specific growth patterns. These results demonstrate the effectiveness of high-resolution remote sensing and advanced analytical models for detailed characterization of complex coastal marsh ecosystems, supporting both ecological understanding and local conservation planning.

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the use of remote sensing, including satellite imagery, aerial surveys, and near-surface observations, to monitor ice cover development in the Beauharnois Canal along the St. Lawrence River. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019-2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms . Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. These findings demonstrate the value of remote sensing in river ice modelling and potential applications to support operational decision-making for regulated river systems.

When heavy snow or thinning ice allows seawater to intrude into the snow–ice interface, a saline slush layer forms, softening the surface and reducing traction. Because flooding is often invisible, travelers risk becoming stuck in remote areas, creating hazardous conditions. Saline slush also alters the snowpack’s physical and electromagnetic properties. Increased liquid water and salinity affect microwave signal interactions, complicating the estimation of ice properties using remote sensing. Depending on snow depth, temperature, and salinity, slush may refreeze or remain unfrozen, influencing ice thickness and heat transfer. Synthetic Aperture Radar (SAR) is widely used to monitor sea ice under all weather and light conditions. Its signal penetrates the dry snowpack and respond to changes at the snow base, making SAR suitable for detecting seawater flooding. However, SAR observations are sensitive to the target dielectric properties, surface roughness, frequency, incidence angle, and environmental variability. L-band coherence has shown sensitivity to flooding, but its behaviour on snow-covered ice remains poorly understood. This study examines the relationship between seawater flooding and SAR coherence using X- and L-band data collected alongside 2024–2025 field measurements in Qikiqtarjuaq, Nunavut. This research will show how SAR coherence can reveal flooded ice, supporting safer travel in northern communities.

River ice is a key variable in northern regions, with impacts on transportation, infrastructure and flood events. There is increasing emphasis on using remote sensing data to assist operational monitoring. This study investigates the use of synthetic aperture radar (SAR) data for this purpose. The main goal is to provide an open, accessible and scalable approach for accurate semantic segmentation of SAR data into ice and water classes.

Snow Water Equivalent (SWE) refers to the quantity of water contained within the snowpack, which is a critical component of the seasonal water cycle in cold regions, notably Canada. The Gravity Recovery and Climate Experiment (GRACE) mission primarily focuses on quantifying Terrestrial Water Storage Anomalies (TWSA),

which is the sum of anomalies in groundwater, soil moisture, surface water, and snow/ice. Separating the individual components with high precision is a challenging task due to the complex interactions of these parameters and their uncertainties involved. This study proposes an enhanced estimator which is modified based on the

spectral combination theory, to extract the SWE component from GRACE/GRACE-FO (Follow-On) TWS measurements. This estimator uses a hydrological model and its uncertainty to optimally extract the SWE component from the GRACE monthly models in spectral domain. The approach was applied in eight selected basins across

Canada, covering a diverse range of climatic and geographical conditions. Different winter seasons of each basin were considered, including the peak accumulation and ablation phases of the snowpack, from January 2003 to the end of 2022.

During the winters of 2023-2024 and 2024-2025, machine learning (ML) based models were implemented to predict ice thickness at eight sites on the Churchill River and Lake Melville, Labrador for one- and three-day horizons. The forecast ice thicknesses were fed into the Churchill River Flood Forecasting System (CRFFS) operated by the Newfoundland and Labrador (NL) provincial government’s Water Resources Management Division (WRMD). The models were trained on measured ice thickness data from 2017-2023, with the 2024-2025 models additionally trained with data from the 2023-2024 ice season. The 2023-2024 models were deep learning models that used Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs), and the 2024-2025 models were ML models that used a simpler gradient boosting regression (GBR) algorithm. The LSTM (2023-2024) models used a running time-series of local meteorological observations as predictor variables to directly forecast ice thickness, and the GBR (2024-2025) models mainly used forecast surface energy balance variables to predict changes in ice thickness. The average performance of the models across the eight sites was comparable between the two ice seasons; however, the 2024-2025 season models improved performance at key sites on the Churchill River that are critical to ice jam flood forecasting. This paper describes the development of the models and their operation and comparative performance over the 2023-2025 ice seasons.

Accurate, reliable, and early-warning forecasts of river ice breakup are essential for flood risk mitigation and public safety, particularly in relation to river transportation and ice road operations. Synthetic Aperture Radar (SAR) satellite imagery has been widely utilized for monitoring river ice conditions due to its sensitivity to surface roughness and dielectric properties. This study advances traditional SAR applications and, to our knowledge, presents the first model that directly incorporates SAR data as input within a machine learning (ML) framework for river ice breakup prediction. The method leverages the correlation between SAR backscatter dynamics and the onset of surface melt. The model was evaluated using leave-one-out cross-validation, achieving an overall accuracy of 92%, an F1-score of 0.91, a Kappa coefficient of 0.84, and a mean absolute error (MAE) of less than 6 days for both the two- and three-week forecasts. The algorithm was also implemented in near-real-time operational settings, demonstrating strong performance with MAE values ranging from zero to four days across different river segments. The approach was further tested on an independent site, where it maintained robust predictive skill. The newly developed method shows strong potential for two- and three-week forecasting of river ice breakup, offering a scalable, cost-effective, and operationally viable tool for management and early warning applications.

Forest structural complexity (FSC) reflects the three-dimensional arrangement and distribution of forest elements and serves as a key ecological indicator of biodiversity and forest productivity. Decades of overharvesting have transformed many temperate rainforests into young, homogeneous stands. Given the central role of FSC in ecosystem functioning, silvicultural strategies increasingly aim to retain or enhance structural complexity and mitigate the ecological impacts of timber harvesting. Monitoring structural development across silvicultural treatments, environmental gradients, and disturbance regimes is therefore essential. However, large-scale assessments of FSC remain limited. In this study, we evaluate the scalability of canopy entropy (CE), a LiDAR-derived FSC index, using deep learning applied to multisensor radar and optical imagery. We trained a U-Net convolutional neural network using airborne LiDAR-derived CE as the reference variable and Sentinel-1 and Sentinel-2 data as wall-to-wall predictors. The model demonstrated strong overall predictive performance (R² = 0.80, MAE = 0.09, bias = 0.02, nRMSE = 12.2%). However, the horizontal complexity component of CE (CExy) exhibited substantially lower accuracy. Although aspects of horizontal complexity may be indirectly inferred from vertical structure or canopy cover, CE should be interpreted with caution. Future work should focus on improving the representation of horizontal complexity. Despite these limitations, the resulting CE map provides a foundation for evaluating silvicultural practices and identifying structurally complex forests with high conservation value.

Wildfire severity, often correlated with biomass loss, has increased since the 1980s, driving greater biomass depletion across landscapes. Canada's 2023 wildfire season burned over 15 million hectares and released 647 TgC of carbon, surpassing most nations' annual emissions. This trend underscores the need for scalable aboveground biomass (AGB) monitoring for greenhouse gas estimation. While LiDAR has improved AGB estimation, airborne systems remain costly with limited spatial coverage. Researchers have addressed this by scaling LiDAR-derived estimates to satellite imagery for broader monitoring. However, current scaling paradigms are developed predominantly for closed-canopy forests, with limited evaluation in open-canopy ecosystems like peatlands, despite their high fire severity and disproportionate carbon contributions when burned. Peatlands pose unique challenges: low and spatially heterogeneous AGB, open canopies that allow soil and water to obfuscate satellite signals, and non-linear structural-biomass relationships in sparse vegetation.

This systematic review and meta-analysis examines the accuracy of scaling LiDAR-derived AGB estimates to optical and radar satellite imagery across peatlands and structurally analogous ecosystems, including tropical savannas, floodplain forests, mangroves, and arctic shrublands. We searched Scopus and Google Scholar using a four-block query, yielding 271 peer-reviewed studies. Using a random-effects model, R² values were transformed to Fisher's Z scores, and heterogeneity was quantified using the I² statistic.

Preliminary analysis revealed no significant difference between modelling approaches and target ecosystem. Heterogeneity was minimal, indicating model type and ecosystem type exert limited influence on accuracy outcomes. Full dataset analysis is ongoing.

Habitat suitability models are central to conservation

planning, species management, and landscape-level decision

support. Continuous Landscape Inventory (CLI)

datasets provide stand-level forest attributes (species mix,

height, basal area, crown closure, age, disturbance history)

that are rarely used at scale together with satellitederived

biophysical indicators for operational habitat mapping.

This work proposes a replicable workflow that fuses

provincial CLI with multisensor satellite data (Sentinel-

2 MSI, Landsat series, and SAR-derived structure proxies)

and environmental layers (elevation, distance-to-water,

road density) to produce fine-scale habitat suitability surfaces

across New Brunswick, Canada.

Wildfires significantly impact forest ecosystems by disrupting carbon cycles, with effects varying based on fire intensity and forest bio-physical characteristics such as vegetation types, structures, topography, and climate. These factors collectively influence fire spread, biomass reduction, and post-fire vegetation regrowth, making it crucial to accurately quantify wildfire impacts on forest carbon dynamics for understanding terrestrial-atmosphere interactions and global climate implications.

This study uses wildfires in California's mountainous forests as a case study, employing two aboveground biomass (AGB) datasets—one derived from remote sensing data and the other from process-based ecological models—to assess wildfire impacts on forest carbon stocks. Remote sensing-based indices, while effective in detecting spectral changes, often fall short in quantifying biophysical alterations, particularly carbon dynamics. Conversely, process-based models adhere to ecological principles but may not fully capture fire-induced carbon changes. Our analysis reveals significant variations in post-fire disturbance and recovery patterns based on fire severity, elevation, and forest type. The remote sensing dataset showed faster initial recovery, likely due to herbaceous vegetation greening, while the ecological model dataset indicated slower, more stable recovery, reflecting delayed tree regeneration. These findings underscore the necessity of integrating multi-source datasets to accurately capture post-fire carbon dynamics.

Wildfire Early Warning Systems (EWS) are increasingly essential as climate-driven extreme fire events grow in frequency

and severity. Yet their maturity and operational robustness vary widely across countries due to differences in

resources, data infrastructure, and institutional capacity. This study conducts a systematic global assessment of wildfire

EWS across high-, middle-, and low-income nations, evaluating how multisensor Earth Observation (EO) data and predictive

intelligence are integrated into functional early warning and decision-support systems. A transparent benchmarking

framework is introduced with two core pillars: (i) multisensor geospatial monitoring—assessing temporal resolution,

spectral sensitivity, spatial detail, and GEO–LEO fusion; and (ii) hotspot intelligence and predictive modeling—evaluating

model class, forecast range, validation practices, and real-time operational performance. These pillars are complemented

by an impact-readiness layer aligned with the Sendai Framework, linking hazard detection to exposure, vulnerability, and

alert dissemination.

Results show strong stratification by income. High-income countries achieve near–real-time hotspot detection, GEO–LEO

data fusion, and validated multi-day behaviour forecasts. Middle-income nations display transitional but uneven progress,

while low-income countries rely almost exclusively on global detection platforms, highlighting institutional, not technological,

bottlenecks. Canada’s EWS landscape is evaluated, highlighting gaps in accessibility, standardization, and

timeliness of EO-derived intelligence. Opportunities for strengthening Canada’s system include adoption of emerging EO

technologies, improved fuel characterization, next-generation hybrid physics–ML/QML behaviour modeling, integrated

national decision-support platforms, and enhanced FireSmart community interfaces.

Overall, the study provides a scalable global framework for comparing national wildfire EWS maturity, identifying

investment priorities, and guiding future improvements.

This project explores how drones and deep learning can help monitor the recovery of former aggregate and mining sites. Traditional methods for assessing land restoration such as field surveys and satellite imagery are often time-consuming, expensive, and limited in detail. Using high-resolution drone imagery and a compact deep learning model, this study offers a faster and more flexible way to track how vegetation and land cover change over time.

The approach classifies ground surfaces into three simple categories: healthy vegetation, stressed vegetation, and bare soil or rock - providing clear indicators of how well a site is recovering after extraction/rehabilitation. Tested at two rehabilitated sites in southern Ontario, the model showed strong and consistent results across different months of the growing season, even using only standard colour drone imagery.

This work highlights how drone-based monitoring can make ecological restoration assessment more efficient, objective, and repeatable. Once trained, the model can quickly analyze new imagery without the need for extensive fieldwork, allowing land managers and regulators to identify problem areas and track recovery in near real time. Ultimately, this research points toward a future where rapid, data-driven drone assessments play a role in supporting sustainable land rehabilitation and environmental stewardship.

Deep, persistent drought in 2023 in the Canadian Boreal Plains was associated with wildfires that persisted underground and re-emerged the following spring, a process known as "overwintering" and sometimes called "zombie fires". We analyzed pre-fire Sentinel-2 multispectral imagery of paired 2023-2024 fires to extract any spectral anomalies, with the goal of characterizing conditions conducive to wildfire overwintering. We assessed several spectral indices, including NDVI, GNDVI, EVI, NDMI, TCW, and others relative to a 2016-2022 baseline using the npphen R package.

We found that sites of overwintering fires exhibited moisture anomalies in the spring of 2024, indicating drought conditions that were conducive to the reemergence of overwintering fires. We show how these anomalies were co-located with early season wildfire with an apparent absence of ignition events. Furthermore, we show how in 2024, 25 overwintering wildfires burned 22.8% of the total area burned, while comprising only 1.3% of the total fire count.