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Remote Sens. 2014, 6(4), 3369-3386; doi:10.3390/rs6043369
Article

Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification

1,* , 2
,
3
 and
4
1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 100085, China 2 Department of Plant Sciences, University of California, Davis, Mail Stop 1, 1210 PES, One Shields Ave, Davis, CA 95616, USA 3 Northern Kentucky University, Nunn Drive, Highland Heights, KY 41099, USA 4 Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, USA
* Author to whom correspondence should be addressed.
Received: 10 January 2014 / Revised: 21 March 2014 / Accepted: 26 March 2014 / Published: 16 April 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Abstract

Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches—visual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes.
Keywords: object-based image analysis; visual interpretation; spatial heterogeneity; land cover classification; urban landscape; Baltimore object-based image analysis; visual interpretation; spatial heterogeneity; land cover classification; urban landscape; Baltimore
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhou, W.; Cadenasso, M.L.; Schwarz, K.; Pickett, S.T. Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification. Remote Sens. 2014, 6, 3369-3386.

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