Abstract
Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data and focusing on processing at the scale of ecozone, which represent ecologically distinct regions of Canada. The first and second generations attained relatively highly accurate results with an average approaching 86% though some overestimated wetland extents, particularly of the swamp class. The current research represents a third refinement of the inventory map. It was designed to improve the overall accuracy (OA) and reduce wetlands overestimation by modifying test and train data and integrating additional environmental and remote sensing datasets, including countrywide coverage of L-band ALOS PALSAR-2, SRTM, and Arctic digital elevation model, nighttime light, temperature, and precipitation data. Using a random forest classification within Google Earth Engine, the average OA obtained for the CWIM3 is 90.53%, an improvement of 4.77% over previous results. All ecozones experienced an OA increase of 2% or greater and individual ecozone OA results range between 94% at the highest to 84% at the lowest. Visual inspection of the classification products demonstrates a reduction of wetland area overestimation compared to previous inventory generations. In this study, several classification scenarios were defined to assess the effect of preprocessing and the benefits of incorporating multisource data for large-scale wetland mapping. In addition, the development of a confidence map helps visualize where current results are most and least reliable given the amount of wetland test and train data and the extent of recent landscape disturbance (e.g., fire). The resulting OAs and wetland areal extent reveal the importance of multisource data and adequate test and train data for wetland classification at a countrywide scale.
Highlights
U NTIL recently, the production of large-scale land cover maps through the classification of remote sensing observations required substantial amounts of time, labor, and complex methodologies
An additional 2% improvement is obtained through the inclusion of DEM data
A direct comparison between the accuracy obtained from the pan-Canadian Sentinel-based wetland maps with the Canada-wide Landsat-based map [6] and wetland maps from other sources [7] is impossible, as the accuracies have not been reported from the latter studies, there is a general agreement between areal percentages of wetlands found in this study with the existing literature
Summary
U NTIL recently, the production of large-scale land cover maps through the classification of remote sensing observations required substantial amounts of time, labor, and complex methodologies. The resolution of these maps tended to be coarse due to the nature of historically free remote sensing data, such as MODIS (250 m) and Landsat (30 m) [1] Despite such difficulties and limitations, large-scale land cover data are essential for a broad range of applications related to environmental management, climate change, and the assessment of major habitats. Examples of such land cover data in Canada include the 30 m Annual Crop Inventory (ACI) [2], and the 30 m Land Cover of Canada (LCC) [3], the former spanning the agricultural lands of southern Canada, while the latter spanning the entire country [4]. An estimated 16% of Canada is currently covered in wetlands [7], and given the relatively recent and growing impacts of climate change (permafrost melt, changes to temperature, and precipitation), wetland spatial data at the level of wetland class is an increasing necessity [8]
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