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

Abstract

Abstract. Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of environmental studies as it provides a suite of tools and access to data that facilitate large-scale environmental monitoring projects through its powerful parallel processing capabilities. In this study, we use GEE to access multi-source remote sensing datasets and implement an object-based image analysis, and random forest algorithm for the classification of wetlands in the state of Minnesota. Emergent, forested, and scrub-shrub wetland classes, water, as well as urban, forest, and agriculture land cover types were classified using Sentinel-2, Sentinel-1, USGS 3D Elevation Program 10-meter DEM, and gridded soil data. NDVI, EVI, BSI, NDBI, and NDWI spectral indices were calculated from Sentinel-2 imagery, VV and VH polarization channels, and their ratio, as well as span parameters, were calculated from Sentinel-1 imagery, and slope and aspect features were extracted from DEM. Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), Principal Components Analysis (PCA), and random forest algorithms were implemented to classify wetlands from the GEE platform. Emergent wetlands, water, urban, and agriculture classes performed well with producer accuracies greater than 90%. Sentinel-1, DEM, and soil datasets improve the identification of wetland classes and highlight the importance of multi-source approaches for wetland mapping.

Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

“…The accuracy of the map produced is 89% which is comparable to previous wetland mapping studies [7,[47][48][49][50] especially large scale wetland studies in Minnesota [51,52]. This study leverages Sentinel-2 and Sentinel-1 data for wetland mapping.…”

“…This study leverages Sentinel-2 and Sentinel-1 data for wetland mapping. The integration of multisource data, Sentinel-2, and Sentinel-1, improves wetland classification [11,48,52]. Additionally, the adoption of OBIA helps to further minimize spectral variability within classes, enhancing mapping accuracy [53].…”