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.