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Wetlands are considered one of the most valuable ecosystems around the world and provide numerous environmental services, including water purification, flood protection, and habitat for a variety of species. Wetlands loss is an increasing trend due to anthropogenic activities and natural processes. As such, spatial knowledge regarding the extent and dynamics of surface water is demanding for wetland conservation and protection. The Landsat program, with five decades of historical Earth observation data, has a unique advantage for monitoring wetland surface water changes and dynamics with 30 m spatial resolution. We monitored 266 Ramsar wetland sites in North America for the past 40 years using the open-access Landsat data within the Google Earth Engine cloud computing platform. Landsat Collection 2 Level-2 surface reflectance products were preprocessed and cloud-screened, and a time series of spectral bands and indices were created. The unsupervised Dynamic Surface Water Extent method classified each image into water classes with different confidence levels. An average overall agreement of 92,97% and an average F-score of 96.31% were achieved in this study. Water occurrence maps, in addition to inundation class and change maps, were created for the entire North America, and quantified spatial information was calculated for Ramsar wetland sites.

Due to the advent of powerful parallel processing tools, including modern Graphics Processing Units (GPU), new deep learning algorithms, such as Convolutional Neural Networks (CNNs), have significantly altered the state-of-the-art algorithms in satellite classification of complex environments. Recent studies have demonstrated that the generic feature maps extracted from CNNs are incredibly effective in wetland classification. The main drawback of very deep CNNs is described as structurally complex, causing the need for extensive training data. To address deep Convolutional Neural Network’s limitations, a timely and computationally efficient CNN architecture is proposed in this paper. The results of the proposed model were compared to other well-known CNNs (i.e., GoogleNet and SqueezeNet) and several machine learning algorithms, including Random Forest, Gaussian Naïve Bayes, and the Bayesian Optimized Tree. Results showed while significantly reduced the training time, the proposed deep learning method outperformed GoogleNet and SqueezeNet by about 12.71% and 12.2% in terms of mean overall accuracy, respectively. The classification results shown that the accuracy of wetland classes (fen, marsh, swamp, and shallow water) were significantly improved by applying the proposed CNN method.

Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. Iin order to increase wetland classification accuracy, the main contributions of this new study are adding more training data to the classification process and training Random Forest (RF) models on the Google Earth Engine (GEE) platform within the boundaries of ecozones rather than provinces. A considerable effort has been devoted to data collection, preparation, standardization of datasets for each ecozone. The data cleaning reveals a data gap in several Northern ecozones. Accordingly, high-resolution optical data, from Worldview-2 and Pleiades, were acquired to delineate wetland training data based on visual interpretation in those regions. By using this well-distributed training data, this second generation wetland inventory map represents an improvement of 7% compared to the first generation map. Accuracy varied from 76% to 91% in different ecozones depending on available resources. Furthermore, the results of RF variable importance, which was carried out for each ecozone, demonstrate that and NDVI extracted from Sentinel-1 and Sentinel-2 data, respectively, were the most important features for wetland mapping.

Detailed information on the spatial distribution of wetlands is crucial for sustainable management and resource assessment. Furthermore, regularly updated wetland inventories are of particular importance given that wetlands comprise a dynamic, rather than permanent, land condition. Accordingly, satellite-derived wetland maps are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. Leveraging state-of-the-art remote sensing data and tools, this study produces a high-resolution 10-m wetland inventory map of Canada, covering an approximate area of one billion hectares, using multi-year, multi-source (Sentinel-1 and Sentinel-2) Earth Observation (EO) data on the Google Earth Engine™ cloud computing platform. The whole country is mapped using a large volume of reference samples using an object-based random forest classification scheme with an overall accuracy approaching 80% and individual accuracies varying from 74% to 84% in different provinces. This nationwide wetland inventory map illustrates that 19% of Canada’s land area is covered by wetlands, most of which are peatlands dominate in the northern ecozones. Importantly, the resulting ever-demanding wetland inventory map of Canada provides unprecedented details on the extent, status, and spatial distribution of wetlands and thus, is useful for many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants.

The production of spatially detailed quantitative maps of water level variations in flooded vegetation, and the detection of flow patterns and discontinuities in both managed and natural wetland ecosystems provide valuable information for monitoring these unique environments. Hydrological monitoring of wetlands is also critical for maintaining and preserving the habitat of various plant and animal species. Over the last two decades, advances in remote sensing technologies have supported wetland monitoring and management in several aspects, including classification, change detection, and water level monitoring. In particular, Interferometric Synthetic Aperture Radar (InSAR) has emerged as a promising tool for hydrological monitoring of wetland water bodies. However, a comprehensive review of the status, trends, techniques, advances, potentials, and limitations of this technique is lacking. In this study, we evaluate the use of InSAR for hydrological monitoring of wetlands, discuss the main challenges associated with this technique, recommend possible solutions to mitigate the main problems identified in the literature, and present opportunities for future research. RÉSUMÉ La production de cartes quantitatives détaillées de la variation du niveau d’eau de la végétation submergée et la détection des régimes d’écoulement et des discontinuités fournissent des renseignements importants sur les milieux humides, tant à l’état naturel que dans les zones aménagées. La surveillance hydrologique des milieux humides est essentielle au maintien et à la préservation de l’habitat de la faune et de la flore. Au cours des deux dernières décennies, les progrès techniques en télédétection ont contribué à la surveillance et à la gestion des milieux humides à plusieurs égards, dont la classification, la détection des changements et le suivi du niveau d’eau. En particulier, l’interférométrie par radar à synthèse d'ouverture (InSAR) s’est avérée un instrument prometteur pour la surveillance hydrologique des étendues d’eau en milieu humide. Il manque cependant une revue exhaustive de l'état, des tendances, des techniques, des avancés, du potentiel et des limitations de cette approche. Dans la présente étude, nous évaluons l’utilisation de l’InSAR pour la surveillance hydrologique des milieux humides, nous discutons des défis liés à cette technique, nous recommandons des solutions potentielles pour mitiger les problèmes principaux identifiés dans la littérature et nous présentons des possibilités de recherche future.

Speckle noise significantly degrades the radiometric quality of PolSAR image and, consequently, decreases the classification accuracy. This article proposes a new speckle reduction method for PolSAR imagery based on an adaptive Gaussian Markov Random Field model. We also introduce a new span image, called pseudo-span, obtained by the diagonal elements of the coherency matrix based on the least square analysis. The proposed de-speckling method was applied to full polarimetric C-band RADARSAT-2 data from the Avalon area, Newfoundland, Canada. The efficiency of the proposed method was evaluated in 2 different levels: de-speckled images and classified maps obtained by the Random Forest classifier. In terms of de-speckling, the proposed method illustrated approximately 19%, 43%, 46%, and 50% improvements in equivalent number of looks values, in comparison with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. Also, improvements of approximately 19%, 9%, 55%, and 32% were obtained in the overall classification accuracy using de-speckled PolSAR image by the proposed method compared with SARBM3D, Enhanced Lee, Frost, and Kuan filter, respectively. This new adaptive de-speckling method illustrates to be an efficient approach in terms of both speckle noise suppression and details/edges preservation, while having a great influence on the overall wetland classification accuracy.

Synthetic aperture radar (SAR) compact polarimetry (CP) systems are of great interest for large area monitoring because of their ability to acquire data in a wider swath compared to full polarimetry (FP) systems and a significant improvement in information content compared to single or dual polarimetry (DP) sensors. In this study, we compared the potential of DP, FP, and CP SAR data for wetland classification in a case study located in Newfoundland, Canada. The DP and CP data were simulated using full polarimetric RADARSAT-2 data. We compared the classification results for different input features using an object-based random forest classification. The results demonstrated the superiority of FP imagery relative to both DP and CP data. However, CP indicated significant improvements in classification accuracy compared to DP data. An overall classification accuracy of approximately 76% and 84% was achieved with the inclusion of all polarimetric features extracted from CP and FP data, respectively. In summary, although full polarimetric SAR data provide the best classification accuracy, the results demonstrate the potential of RADARSAT Constellation Mission for mapping wetlands in a large landscape.