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

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2014-04, Vol.6 (4), p.3369-3386
• Main Authors: Zhou, Weiqi, Cadenasso, Mary L, Schwarz, Kirsten, Pickett, Steward TA
• Format: Article
• Language: English
• Subjects: Baltimore; Classification; Ecological research; Ecology; Heterogeneity; Image analysis; Land cover; land cover classification; object-based image analysis; Remote sensing; spatial heterogeneity; Urban environments; urban landscape; Visual; visual interpretation; Watersheds
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs6043369

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.