Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km²). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here.

In this study, a new method is proposed for semi-automated surface water detection using synthetic aperture radar data via a combination of radiometric thresholding and image segmentation based on the simple linear iterative clustering superpixel algorithm. Consistent intensity thresholds are selected by assessing the statistical distribution of backscatter values applied to the mean of each superpixel. Higher-order texture measures, such as variance, are used to improve accuracy by removing false positives via an additional thresholding process used to identify the boundaries of water bodies. Results applied to quad-polarized RADARSAT-2 data show that the threshold value for the variance texture measure can be approximated using a constant value for different scenes, and thus it can be used in a fully automated cleanup procedure. Compared to similar approaches, errors of omission and commission are improved with the proposed method. For example, we observed that a threshold-only approach consistently tends to underestimate the extent of water bodies compared to combined thresholding and segmentation, mainly due to the poor performance of the former at the edges of water bodies. The proposed method can be used for monitoring changes in surface water extent within wetlands or other areas, and while presented for use with radar data, it can also be used to detect surface water in optical images.

For this research, the Random Forest (RF) classifier was used to evaluate the potential of simulated RADARSAT Constellation Mission (RCM) data for mapping landcover within peatlands. Alfred Bog, a large peatland complex in Southern Ontario, was used as a test case. The goal of this research was to prepare for the launch of the upcoming RCM by evaluating three simulated RCM polarizations for mapping landcover within peatlands. We examined (1) if a lower RCM noise equivalent sigma zero (NESZ) affects classification accuracy, (2) which variables are most important for classification, and (3) whether classification accuracy is affected by the use of simulated RCM data in place of the fully polarimetric RADARSAT-2. Results showed that the two RCM NESZs (−25 dB and −19 dB) and three polarizations (compact polarimetry, HH+HV, and VV+VH) that were evaluated were all able to achieve acceptable classification accuracies when combined with optical data and a digital elevation model (DEM). Optical variables were consistently ranked to be the most important for mapping landcover within peatlands, but the inclusion of SAR variables did increase overall accuracy, indicating that a multi-sensor approach is preferred. There was no significant difference between the RF classifications which included RADARSAT-2 and simulated RCM data. Both medium- and high-resolution compact polarimetry and dual polarimetric RCM data appear to be suitable for mapping landcover within peatlands when combined with optical data and a DEM.

Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring into the future. Results show that integration of data from multiple sensors is most effective for monitoring coastal wetlands in the Great Lakes region. While products developed using methods described in this article provide valuable management tools, more effort is needed to reach the goal of establishing a dynamic, near-real-time, remote sensing-based monitoring program for the basin.

Detailed information on the land cover types present and the horizontal position of the land–water interface is needed for sensitive coastal ecosystems throughout the Arctic, both to establish baselines against which the impacts of climate change can be assessed and to inform response operations in the event of environmental emergencies such as oil spills. Previous work has demonstrated potential for accurate classification via fusion of optical and SAR data, though what contribution either makes to model accuracy is not well established, nor is it clear what shorelines can be classified using optical or SAR data alone. In this research, we evaluate the relative value of quad pol RADARSAT-2 and Landsat 5 data for shoreline mapping by individually excluding both datasets from Random Forest models used to classify images acquired over Nunavut, Canada. In anticipation of the RADARSAT Constellation Mission (RCM), we also simulate and evaluate dual and compact polarimetric imagery for shoreline mapping. Results show that SAR data is needed for accurate discrimination of substrates as user’s and producer’s accuracies were 5–24% higher for models constructed with quad pol RADARSAT-2 and DEM data than models constructed with Landsat 5 and DEM data. Models based on simulated RCM and DEM data achieved significantly lower overall accuracies (71–77%) than models based on quad pol RADARSAT-2 and DEM data (80%), with Wetland and Tundra being most adversely affected. When classified together with Landsat 5 and DEM data, however, model accuracy was less affected by the SAR data type, with multiple polarizations and modes achieving independent overall accuracies within a range acceptable for operational mapping, at 89–91%. RCM is expected to contribute positively to ongoing efforts to monitor change and improve emergency preparedness throughout the Arctic.