The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.

The authors evaluated multiple remotely sensed datasets for their contributions to operational wetland mapping in a subarctic, boreal cordillera study site in Yukon, Canada. They assessed Sentinel-2 optical imagery, Sentinel-1 C-band and ALOS PALSAR L-band synthetic aperture radar (SAR) imagery, and topographical data from the territorial digital elevation model (DEM) using an object-based image analysis (OBIA) approach. Three machine-learning algorithms were tested, namely random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN), using various data combinations (11 model scenarios). RF produced the most accurate results when incorporating all optical, SAR and DEM data (86.5%, kappa 0.84), with open water (100% producer accuracy, PA), marsh (75% PA) and swamps (85.7% PA) being detected most accurately. When assessed in isolation, Sentinel-2 optical data consistently generated more accurate classifications than either SAR platform or DEM data. RF variable importance metrics provided further explanation to these results, indicating the 8 most powerful variables to be optical. Variable reduction tests also produced comparable accuracies, indicating that an optimal RF model can be built based on predictive power rankings. The results can be used to inform resource managers on the efficacy of current datasets and their applications to wetland mapping in northern, subarctic environments.

Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost-, time-, and computationally efficient approach. Although the initial effort to produce an CWI map was valuable with a 71% Overall Accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental non-profit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in-situ data, photo-interpreted reference samples, Land Cover/Land Use (LCLU) maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in-situ data was 60%. Moreover, including reliable in-situ data, using an object-based classification method, and adding more optical and Synthetic Aperture RADAR (SAR) datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.

Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that is in decline due to numerous natural and anthropogenic pressures. Satellite Synthetic Aperture Radar is a popular and inexpensive solution for such exercises over large scales through the application of thresholds to distinguish water from non-water. Despite improvements to threshold methods, threshold selection is traditionally manual, which introduces subjectivity and inconsistency over large scales. This study presents a novel method for objectively determining and applying a threshold to determine water masks from Synthetic Aperture Radar (SAR) imagery on a scene-by-scene basis. The method was applied to Radarsat-2 and simulated Radarsat Constellation Mission scenes, and validated against two independent validation sources with high accuracy (Kappa ranging from 0.85 to 0.93). Expectedly, greatest misclassification occurs near shorelines, which are often ecologically important zones. Comparisons between Radarsat-2 and Radarsat Constellation Mission thresholds and outputs suggest that the latter is a capable successor for surface water applications. This work represents a foundational step toward objectivity and consistency in large-scale water mapping and monitoring.