The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W5-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W5-2021, 313–319, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W5-2021, 313–319, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W5-2021-313-2021

  23 Dec 2021

23 Dec 2021

A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE

A. Jamali1, M. Mahdianpari2,3, and İ. R. Karaş4 A. Jamali et al.
  • 1Karabük University, Department of Civil Engineering, Faculty of Engineering, Karabük, Turkey
  • 2Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1B3X5, Canada
  • 3C-CORE, 1 Morrissey Rd, St. John’s, NL A1B 3X5, Canada
  • 4Karabük University, Department of Computer Engineering, Faculty of Engineering, Karabük, Turkey

Keywords: Wetland Mapping, Big data, Sentinel Imagery, Decision Tree, Random Forest, Extreme Gradient Boosting, Google Earth Engine

Abstract. Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers.