Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine
Artikel i vetenskaplig tidskrift, 2023

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).

remote sensing

random forest classification

wetlands

Great Lakes

Google Earth Engine

Författare

Farzane Mohseni

Universität Bonn

Meisam Amani

Henan Polytechnic University

WSP Environment and Infrastructure Canada Limited

Pegah Mohammadpour

Association for the Development of Industrial Aerodynamic

Universidad de Alcala

Mohammad Kakooei

Chalmers, Data- och informationsteknik, Data Science och AI

Shuanggen Jin

Henan Polytechnic University

Shanghai Astronomical Observatory

Armin Moghimi

Leibniz Universität Hannover

Remote Sensing

20724292 (eISSN)

Vol. 15 14 3495

Ämneskategorier

Fjärranalysteknik

Naturgeografi

Datorseende och robotik (autonoma system)

DOI

10.3390/rs15143495

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Senast uppdaterat

2023-08-11