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Open AccessArticle

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

by Mst Ilme Faridatul 1,2 and Bo Wu 1,*
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.
ISPRS Int. J. Geo-Inf. 2018, 7(12), 453;
Received: 7 September 2018 / Revised: 8 November 2018 / Accepted: 21 November 2018 / Published: 22 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
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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.

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