Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results

Abstract

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km2). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

“…All of these demonstrated that deep learning results outperform other machine learning methods such as random forest. Non-neural network methods, such as the work in Amani et al [83], reported a 71% wetland class accuracy across all of Canada. Another study by Mahdianpari et al [34] achieved 88% wetland class accuracy using an object-based random forest algorithm across Newfoundland.…”

“…Most studies are now using a fusion of remote sensing data from SAR, optical, and DEM products [6,7,34,39,49]. The easiest way to access provincial/national-scale data appears to be through Google Earth Engine; thus, many studies use Sentinel-1, Sentinel-2, Landsat, ALOS, or SRTM data [6,7,34,83]. Finally, many machine learning methods have been tested, but it appears that convolutional neural network frameworks produce better, more accurate wetland/landcover classifications [51,52].…”

Species‐Habitat Relationships and Priority Areas for Marsh‐Breeding Birds in Ontario

“…We suspect omission of these local within‐wetland covariates is why our models for sora and Virginia rail performed so poorly, as these species respond strongly to immediate wetland vegetation structure and food availability, and less to wetland and landscape‐scale covariates (Fairbairn and Dinsmore 2001, Naugle et al 2001, Baschuk et al 2012, Tozer 2016, Saunders et al 2019). Developing these covariates and associated improved models and predictive maps are important areas for future research, some of which could potentially be achieved through automated remote sensing (Feyisa et al 2014, Bourgeau‐Chavez et al 2015, Amani et al 2019).…”

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…The GEE greatly improves the processing efficiency when using substantial amounts of remote sensing data. In recent years, the GEE was used in land cover mapping [49][50][51][52][53][54][55][56][57][58], agricultural applications [59][60][61][62][63], disaster management, and earth sciences studies [64][65][66]. This remote sensing data processing cloud platform makes the rapid processing of Sentinel-2 images covering large areas possible.…”