The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

Abstract

Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

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

“…Spatial wetland inventories at a country or provincial scale [30][31][32][33][34] are not new, but having data that are reliable for land management and land planning decisions is a challenge. In Canada, mapping of wetlands via remote sensing is a well-studied topic [35].…”

“…Other studies and projects have used moderate resolution optical data such as Landsat or Sentinel-2 to generate wetland inventories [30,46,47]. Most modern approaches to large-scale wetland inventories utilize a fusion of data such as SAR and optical [34,39,48] and, ideally, SAR, optical, plus topographic information [6,7,49]. Theoretically the fusion of SAR, optical, and topographic information should give the most information on wetlands and wetland class because: (1) SAR is sensitive to the physical structure of vegetation and can detect the dynamic nature of wetlands with a rich time series stack;…”

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.…”

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

“…However, the reliance on human-based expert decisions means that this approach is not suitable for routine monitoring.GEE also has a range of techniques with the potential for developing automatic classification routines. This includes commonly used machine learning approaches such as random forests (RFs) for classifying imagery [39,40]. However, hard classifications of this nature are vulnerable to mixed pixels, especially challenging in wetland environments [39] where the mixture of open water and emergent vegetation or bare soil are commonplace.…”

“…This includes commonly used machine learning approaches such as random forests (RFs) for classifying imagery [39,40]. However, hard classifications of this nature are vulnerable to mixed pixels, especially challenging in wetland environments [39] where the mixture of open water and emergent vegetation or bare soil are commonplace. Alternatively, GEE includes other tools that have the potential for providing the sub-pixel fractional cover of distinctive land cover types such as mixed water and vegetation pixels.…”