Abstract
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 the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the 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 nonprofit, 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 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 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.
Highlights
W ETLANDS are home to a variety of animals and plants
Wetlands are of great importance in Canada because a large portion of the country is covered by these natural resources, which provide valuable benefits to humans and the environment
It was observed that the average overall accuracy (OA) for in situ data, photo-interpreted reference samples, and samples generated from LCLU maps were 60%, 61%, and 54%, respectively
Summary
W ETLANDS are home to a variety of animals and plants. They offer multiple ecosystem services, such as flood risk reduction, soil conservation, water purification, sediment filtration, and much more [1]. Wetland maps resulting from RS methods can be regularly updated based on the temporal frequency of the images used for classification. Another feature of RS that makes it advantageous for wetland mapping applications is the capability of acquiring data from any part of the world, including inaccessible locations where wetlands are often found [1].
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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