Leveraging Google Earth Engine User Interface for Semiautomated Wetland Classification in the Great Lakes Basin at 10 m With Optical and Radar Geospatial Datasets

Abstract

As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts, however current methods that rely on field monitoring are laborintensive, costly, and often outdated. In this study, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 OLI, Sentinel-2 MSI, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May-September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

“…As reviewed in Section 2.3, TCT coefficients have not been published for atmospherically corrected S2 imagery, and Landsat's surface reflectance coefficients [96] do not cover all S2 bands. Whereas certain authors have used at-sensor-derived coefficients on surface reflectance S2 imagery [87][88][89][90][91], in a multi-temporal change application, the ever-changing effects of atmosphere result in inconsistent indices of brightness, greenness, and wetness that are directly a result of fluctuating atmospheric conditions. Although the exact effects onto index values such as dDI are not fully understood, Crist et al [42] suggests that changing atmospheric conditions will alter subsequent results derived from TCT indices.…”

“…Recently, some authors [85,86] have used coefficients developed for Landsat at-sensor reflectance products on S2 level-2A imagery. Others [87][88][89][90][91] have applied TCT coefficients to S2 level-2A data that were developed for at-sensor S2 (level-1C) imagery [43,92]. The spectral range and similarities of bands from Landsat and S2 bands are well documented [93][94][95], and the coefficients as developed by Crist [96] have been used in various studies using surface reflectance Landsat imagery [51,[97][98][99].…”

Flood inundation assessment in the data-scarce region using hydrodynamic models and google earth engine -A case of Ghed region, Ozat River basin, Gujarat, India

“…This data is available from the year 2017 to till date. Analysis with the help of google earth engine is extensively used because of latest machine learning algorithm techniques utilize and low-demanding high-configuration processors (Tamiminia et al 2020;Valenti et al 2020). Although, this research brings for inundation mapping.…”

State-Wide Wetland Inventory Map of Minnesota Using Multi-Source and Multi-Temporalremote Sensing Data

“…Parallel processing capabilities mean that computationally intensive tasks can be distributed across different units to save time and improve efficiency. Google Earth Engine(GEE), a cloud-based platform for processing geographic related information (Tamiminia et al, 2020) is increasingly being used in recent land cover and wetland mapping studies (Mahdianpari et al, 2019(Mahdianpari et al, , 2021Shafizadeh-Moghadam et al, 2021;Tassi & Vizzari, 2020;Valenti et al, 2020). GEE makes multi-source remote sensing workflows easier providing access to multi-petabytes of earth observation data and complementary functionality for implementing algorithms and visualizing results (Shafizadeh-Moghadam et al, 2021;Tamiminia et al, 2020).…”