Next Article in Journal
Public Participation Using 3D Web-Based City Models: Opportunities for E-Participation in Kisumu, Kenya
Next Article in Special Issue
Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest
Previous Article in Journal
Place versus Space: From Points, Lines and Polygons in GIS to Place-Based Representations Reflecting Language and Culture
Previous Article in Special Issue
Direct Impacts of Climate Change and Indirect Impacts of Non-Climate Change on Land Surface Phenology Variation across Northern China
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(12), 453;

Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom 999077, Hong Kong, China
Department of Urban and Regional Planning, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
Author to whom correspondence should be addressed.
Received: 7 September 2018 / Revised: 8 November 2018 / Accepted: 21 November 2018 / Published: 22 November 2018
Full-Text   |   PDF [10855 KB, uploaded 29 November 2018]   |  


Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94–96%, which is superior to the accuracy of the SVM algorithm. View Full-Text
Keywords: terrestrial ecosystem; land cover; classification; spectral indices terrestrial ecosystem; land cover; classification; spectral indices

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Faridatul, M.I.; Wu, B. Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices. ISPRS Int. J. Geo-Inf. 2018, 7, 453.

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



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top