Next Article in Journal
Recent Changes in Terrestrial Gross Primary Productivity in Asia from 1982 to 2011
Previous Article in Journal
Bayesian Networks for Raster Data (BayNeRD): Plausible Reasoning from Observations
Remote Sens. 2013, 5(11), 6026-6042; doi:10.3390/rs5116026

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

1,2,* , 1,2
1 Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China 2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
* Author to whom correspondence should be addressed.
Received: 9 September 2013 / Revised: 10 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013
View Full-Text   |   Download PDF [4632 KB, uploaded 19 June 2014]   |   Browse Figures


Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics.
Keywords: Google Earth; QuickBird; land use/cover; object-based; classification Google Earth; QuickBird; land use/cover; object-based; classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

Hu, Q.; Wu, W.; Xia, T.; Yu, Q.; Yang, P.; Li, Z.; Song, Q. Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping. Remote Sens. 2013, 5, 6026-6042.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


Cited By

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