Next Article in Journal
Groundwater Depletion Estimated from GRACE: A Challenge of Sustainable Development in an Arid Region of Central Asia
Previous Article in Journal
Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery
Previous Article in Special Issue
A Scheme for the Long-Term Monitoring of Impervious−Relevant Land Disturbances Using High Frequency Landsat Archives and the Google Earth Engine
Open AccessArticle

Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping

1
Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan
2
Department of International Environmental and Agricultural Sciences, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan
3
Climate, Biodiversity, Land and Water Department, Food and Agriculture Organization of the United Nations, 00153 Rome, Italy
4
Information and Technology Division (CIO) of Food and Agriculture Organization of the United Nations, 00153 Rome, Italy
5
Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan
6
Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1907; https://doi.org/10.3390/rs11161907
Received: 12 June 2019 / Revised: 6 August 2019 / Accepted: 8 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
  |  
PDF [6855 KB, uploaded 15 August 2019]
  |  

Abstract

In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization’s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets. View Full-Text
Keywords: machine learning; land cover mapping; cloud processing; Google Earth Engine; satellite time series machine learning; land cover mapping; cloud processing; Google Earth Engine; satellite time series
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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mardani, M.; Mardani, H.; De Simone, L.; Varas, S.; Kita, N.; Saito, T. Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping. Remote Sens. 2019, 11, 1907.

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