Next Article in Journal
Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach
Next Article in Special Issue
Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework
Previous Article in Journal
Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature
Previous Article in Special Issue
Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1315; https://doi.org/10.3390/rs9121315

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

1
Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Alberta Biodiversity Monitoring Institute, Edmonton, AB T6G 2E9, Canada
*
Author to whom correspondence should be addressed.
Received: 12 October 2017 / Revised: 30 November 2017 / Accepted: 7 December 2017 / Published: 14 December 2017
(This article belongs to the Special Issue Machine Learning Applications in Earth Science Big Data Analysis)
Full-Text   |   PDF [78885 KB, uploaded 14 December 2017]   |  

Abstract

Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives. View Full-Text
Keywords: cloud computing; machine learning; wetland classification; Sentinel-1; Sentinel-2; digital terrain model; boosted regression trees; topographic wetness index; topographic position index; satellite data streams cloud computing; machine learning; wetland classification; Sentinel-1; Sentinel-2; digital terrain model; boosted regression trees; topographic wetness index; topographic position index; satellite data streams
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Hird, J.N.; DeLancey, E.R.; McDermid, G.J.; Kariyeva, J. Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping. Remote Sens. 2017, 9, 1315.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top