Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources

Challenges of Large-Scale Wetland Classification Using Remote Sensing

verfasst von

Meisam Amani, Brian Brisco, Sahel Mahdavi, Arsalan Ghorbanian, Armin Moghimi, Evan R. Delancey, Michael Merchant, Raymond Jahncke, Lee Fedorchuk, Amy Mui, Thierry Fisette, Mohammad Kakooei, Seyed Ali Ahmadi, Brigitte Leblon, Armand Larocque

Abstract

The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.

Externe Organisation(en)

Wood Environment & Infrastructure Solutions
Canada Center for Mapping and Earth Observation (CCMEO)
K.N. Toosi University of Technology
University of Alberta
Ducks Unlimited Canada
Nova Scotia Department of Lands and Forestry
Manitoba Forestry Branch
Dalhousie University
AgriFood Canada
Babol Noshirvani University of Technology
University of New Brunswick

Typ

Artikel

Journal

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Band

14

Seiten

32-52

Anzahl der Seiten

21

ISSN

1939-1404

Publikationsdatum

2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computer in den Geowissenschaften, Atmosphärenwissenschaften

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://doi.org/10.1109/jstars.2020.3036802 (Zugang: Geschlossen)