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

Remote Sensing

Weiqi Zhou³  Mary. L. Cadenasso¹  Kirsten Schwarz² 

[1] Department of Plant Sciences, University of California, Davis, Mail Stop 1, 1210 PES, One Shields Ave, Davis, CA 95616, USA; E-Mail:;Northern Kentucky University, Nunn Drive, Highland Heights, KY 41099, USA; E-Mail:;State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Shuangqinglu 18, Beijing 100085, China

关键词: object-based image analysis;    visual interpretation;    spatial heterogeneity;    land cover classification;    urban landscape;    Baltimore;   

DOI  :  10.3390/rs6043369

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland