Abstract
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.
Highlights
The boreal zone comprises approximately one-quarter of the world’s wetlands [1]
Numerous sensors exist with specific functionalities, whereby the most common remote sensing platforms used for wetland mapping are typically variations of passive optical imagers, followed by active remote sensing technologies: synthetic aperture radar (SAR) and airborne lidar technology
This review, which is part two of a two part series, examines the use of remote sensing of primarily Canadian boreal wetlands for use in policy and management decision-making. It provides a synthesis of the current state-of-the-art in the discipline of remote sensing for identifying, measuring or inferring proxy-indicators associated with wetland class and extent, condition and processes required for inventory and monitoring within the Ramsar framework [1]
Summary
The boreal zone comprises approximately one-quarter of the world’s wetlands [1]. In Canada, wetlands cover between 18% and 25% of the Canadian boreal region (ECCC, 2016) and are primarily peatlands including bogs and fens [2]. [19] monitored boreal discontinuous permafrost-wetland succession over time using time series airborne lidar data and found that variable rates of wetland expansion were related to spatial variations in incident radiation and underlying hydrological processes These cumulative effects can be related to functional wetland derivatives including vegetation species, structure, productivity and habitat [20,21,22,23]. Remote sensing methods are grouped into wetland processes of importance to the Ramsar Convention on Wetlands [1], which include the broad range of ecosystem services provided by inland wetlands and in particular boreal region wetlands. The overall goal of this review is to better understand connections between individual wetland attributes and processes, end user needs and corresponding remote sensing data products for wetland monitoring and the ‘wise use of wetlands’ identified in the Ramsar Convention framework
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