Conference Agenda

The accumulation of C in peatlands generally depends on hydrologic conditions that maintain saturated soils and impede rates of decomposition. Boreal Peatlands have provided rich reservoirs of stored C for millennia. However, with climate change, warming and drying patterns across the boreal and arctic are resulting in dramatic changes in ecosystems and putting these systems at risk. As long as peatlands are functioning hydrologically, they will continue to sequester and store carbon. The ability to retrieve and monitor soil moisture from peatlands is of interest for a wide range of applications from hydrological modeling to understanding ecosystem vulnerabilities to increased drought, decomposition and wildfire to monitoring methane flux and peatland restoration. To develop soil moisture retrieval algorithms, we studied a range of boreal peatland sites (bogs and fens) stratified across geographic regions of North America from 2010 to 2024. We developed soil moisture retrieval algorithms from polarimetric C-band (5.7 cm wavelength) and L-band (24 cm wavelength) synthetic aperture radar (SAR) data. Both multi-linear regressions and gradient boosters (XGBoost, CatBoost and Explainable Boosting Machines) were developed. We found that integrating polarimetric SAR parameters that are sensitive to vegetation structure and parameters most sensitive to surface soil moisture in the models provided the best results. Data were withheld for model testing and coefficient of determination, RMSE, unbiased RMSE are reported.

The Peace–Athabasca Delta (PAD) is undergoing long-term ecological change driven by climate warming, hydro-regulation, and fluctuating flood–dry cycles. This study uses a harmonised 40-year Landsat composite time series (1984–2024) to assess vegetation, surface-water extent, and thermal conditions across the delta. An 11-year moving-window Mann–Kendall trend analysis was applied to NDVI, EVI, MNDWI, and LST, retaining only significant Theil–Sen slopes. Significant vegetation–water trends were combined into a 10-class framework that maps greening, browning, wetting, and drying across all landscape types, including ecotones. Parallel LST trends reveal reinforcing or contrasting thermal feedbacks. It provides a coherent basis for interpreting whether vegetation and hydrologic changes reflect ecotone expansion or contraction under thermal variability.

Mountain headwaters of the Eastern Slopes of Alberta (ES) are the primary source of freshwater of major easterly flowing basins in western Canada, supplying a significant volume of water to about four million people. However, increasing temperatures is altering mountain aquatic (open water areas, lakes, reservoirs, rivers, ponds, wetlands) and riparian vegetation (herbaceous and woody/shrub) ecosystems. The ES, Canada, has demonstrated landcover and process changes associated with climate warming, e.g., increases in the air temperature [1] have led to earlier snowmelt, and increased glacier wastage, resulting in higher river flows over a shorter period, which can result in expansion of open water areas during and following peak flow periods [2]. The impacts on wetlands are less visible or well known, and there is a need to evaluate spatial and temporal changes and trends in wetland loss, growth, or genesis across this mountainous ecosystem. Here, we provide a framework for quantifying and assessing multi-decadal wetland extents over the large spatial scale of the ES from 1984 to 2023. We used the historical Landsat archive to produce a remote sensing-based time series landcover classification over the last 40 years in the ES.

Functional Data Analysis (FDA) provides a powerful framework for representing temporal dynamics in remote-sensing data. Building on this concept, this study develops a transfer learning framework using a minimally trained Functional Principal Component Analysis (FPCA)-based feature extraction engine (“FPC engine”) to map dynamic wetlands at large scale. A small set of training locations from Ontario was used to train the FPC engine, which captures dominant seasonal backscatter patterns of open water, shallow water, and marsh-like vegetation. The trained engine was then transferred to the Prairie Pothole Region (PPR) to delineate dynamic wetland classes without extensive local calibration. This label-efficient design—supervised in selecting training locations but unsupervised in feature extraction—reduces field data needs while maintaining strong generalization. Validated results show that the transferred FPC engine effectively separates dynamic wetland classes across contrasting climatic and geomorphic conditions, supporting scalable and cost-efficient monitoring with Sentinel-1 SAR data.

Wetland mapping in boreal environments remains challenging due to complex vegetation structure, subtle and variable terrain gradients, diverse wetland types, and the proportion of treed wetlands. This study develops and evaluates a framework to remotely identify wetland types in Pukaskwa National Park (Ontario, Canada) by integrating multi-scale terrain metrics with multi-temporal Sentinel-2 spectral derivatives. Five years (2017–2021) of Sentinel-2 data were used to derive harmonic NDVI metrics, including linear trend, amplitude, and phase of the first Fourier component, capturing seasonal vegetation and hydrologic dynamics. These spectral predictors effectively delineated open water and non-treed peatlands but struggled in densely forested wetlands where canopy obscures surface moisture signals. To address this limitation, Gaussian scale-space analysis was applied to the Copernicus GLO-30 DSM, informed by FFT-based evaluation of terrain wavelengths (100 m–10 km), to generate multi-scale Local Relief Models and curvature metrics representing depressional and convex landforms. A hierarchical workflow masked open water using Sentinel-1, removed upland convex terrain using LRM-curvature rules, then applied Random Forest classification using field training data and combined spectral-terrain predictors. Accuracy assessment stratified by terrain context showed strong performance in low-lying depressional areas and suppression of false wetland detections in high terrain with local depressions. Reduced accuracy in relatively flat areas was attributed to DSM vertical uncertainty limiting detection of shallow depressions beneath dense canopy, resulting in reliance on optical separability that weakens under closed canopy but improves where tree cover is sparse. Overall, results demonstrate the value of combining Fourier-based temporal descriptors with multi-scale terrain analysis for boreal wetland mapping.