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

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Received: 10 January 2014; in revised form: 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))
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.
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
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MDPI and ACS Style

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.

AMA Style

Zhou W, Cadenasso ML, Schwarz K, Pickett ST. Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification. Remote Sensing. 2014; 6(4):3369-3386.

Chicago/Turabian Style

Zhou, Weiqi; Cadenasso, Mary. L.; Schwarz, Kirsten; Pickett, Steward T. 2014. "Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification." Remote Sens. 6, no. 4: 3369-3386.

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