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Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas

Citation

Mathieu, R and Aryal, J and Chong, AK, Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas, Sensors, 2007, (7) pp. 2860-2880. ISSN 1424-8220 (2007) [Refereed Article]


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Copyright Statement

Copyright 2007 the authors and MDPI Licenced under Creative Commons Attribution 3.0 Unported (CC BY 3.0) http://creativecommons.org/licenses/by/3.0/

DOI: doi:10.3390/s7112860

Abstract

Effective assessment of biodiversity in cities requires detailed vegetation maps. To date, most remote sensing of urban vegetation has focused on thematically coarse land cover products. Detailed habitat maps are created by manual interpretation of aerial photographs, but this is time consuming and costly at large scale. To address this issue, we tested the effectiveness of object-based classifications that use automated image segmentation to extract meaningful ground features from imagery. We applied these techniques to very high resolution multispectral Ikonos images to produce vegetation community maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and a multi-scale segmentation algorithm used to produce a hierarchical network of image objects. The upper level included four coarse strata: industrial/commercial (commercial buildings), residential (houses and backyard private gardens), vegetation (vegetation patches larger than 0.8/1ha), and water. We focused on the vegetation stratum that was segmented at more detailed level to extract and classify fifteen classes of vegetation communities. The first classification yielded a moderate overall classification accuracy (64%, = 0.52), which led us to consider a simplified classification with ten vegetation classes. The overall classification accuracy from the simplified classification was 77% with a value close to the excellent range ( = 0.74). These results compared favourably with similar studies in other environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.

Item Details

Item Type:Refereed Article
Research Division:Environmental Sciences
Research Group:Environmental Science and Management
Research Field:Conservation and Biodiversity
Objective Division:Environment
Objective Group:Flora, Fauna and Biodiversity
Objective Field:Urban and Industrial Flora, Fauna and Biodiversity
Author:Aryal, J (Dr Jagannath Aryal)
ID Code:80441
Year Published:2007
Web of Science® Times Cited:80
Deposited By:Geography and Environmental Studies
Deposited On:2012-10-31
Last Modified:2012-11-22
Downloads:230 View Download Statistics

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