期刊
REMOTE SENSING
卷 6, 期 4, 页码 3369-3386出版社
MDPI AG
DOI: 10.3390/rs6043369
关键词
object-based image analysis; visual interpretation; spatial heterogeneity; land cover classification; urban landscape; Baltimore
类别
资金
- National Natural Science Foundation of China [41371197]
- One hundred talents. program of Chinese Academy of Sciences
- National Science Foundation [DEB-0844778, DEB 042376, BCE 0508054]
- Direct For Biological Sciences [0844778] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Division Of Environmental Biology [1027188] Funding Source: National Science Foundation
- Division Of Environmental Biology [0844778] Funding Source: National Science Foundation
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 approachesvisual 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.
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