Object-based urban landcover mapping methodology using high spatial resolution imagery and airborne laser scanning

David A. R. Williams; Giona Matasci; Nicholas C. Coops; Sarah E. Gergel

doi:10.1117/1.JRS.12.046020

10 November 2018 Object-based urban landcover mapping methodology using high spatial resolution imagery and airborne laser scanning

David A. R. Williams, Giona Matasci, Nicholas C. Coops, Sarah E. Gergel

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 4, 046020 (November 2018). https://doi.org/10.1117/1.JRS.12.046020

Abstract

Mapping landcover in cities is essential for urban ecology and landuse management, yet urban landcover is often highly heterogeneous at fine spatial scales. Pixel-based approaches are shown to be less successful for effectively mapping urban landcover due to high heterogeneity, with relatively low accuracies reported despite the use of high spatial resolution optical imagery. Alternatively, geographic object-based image analysis (GEOBIA) has yielded higher accuracies across a range of urban applications. We combine three-dimensional (3-D) information from airborne laser scanning (ALS) data with RapidEye high-spatial-resolution imagery in a GEOBIA approach to classify urban landcover in a large metropolitan region in Vancouver, Canada. Results indicate that 12 urban classes could be accurately mapped at 2-m spatial resolution across 150,000 ha with an overall accuracy of 88% (kappa 0.87). Though 5-m RapidEye multispectral pixels were often mixed in heterogeneous urban areas, the additional insight provided by the 3-D ALS information enabled accurate classification of fine spatial objects such as street trees and single-family dwellings.

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David A. R. Williams, Giona Matasci, Nicholas C. Coops, and Sarah E. Gergel "Object-based urban landcover mapping methodology using high spatial resolution imagery and airborne laser scanning," Journal of Applied Remote Sensing 12(4), 046020 (10 November 2018). https://doi.org/10.1117/1.JRS.12.046020

Received: 11 May 2018; Accepted: 9 October 2018; Published: 10 November 2018

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