A brightness–darkness–greenness model for monitoring urban landscape evolution in a developing country – A case study of Shanghai

• Published in: Landscape and urban planning 2014-07, Vol.127, p.13-17
• Main Authors: Yue, Wenze, Ye, XinYue, Xu, Jianhua, Xu, Lihua, Lee, Jay
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Brightness–darkness–greenness (B–D–G) model; Conservation, protection and management of environment and wildlife; Dynamic tests; Dynamics; Evolution; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Techniques; Monitoring; Remote sensing; Shanghai; Spectra; Teledetection and vegetation maps; Urban environments; Urban remote sensing; Vegetation–impervious surface–soil (V–I–S) model
• ISSN: 0169-2046; 1872-6062
• DOI: 10.1016/j.landurbplan.2014.04.010

•We developed a remotely sensed based brightness–darkness–greenness (B–D–G) model.•We used Shanghai as a case study to explore the application of B–D–G model.•The B–D–G model can reveal urban landscape composition dynamics.•We detected urban renewal by using the B–D–G model. To monitor and model the evolution of urban landscapes, we develop a brightness–darkness–greenness (B–D–G) model. It is based on the vegetation–impervious surface–soil (V–I–S) model, proposed by Ridd (1995) to simplify urban environments to three basic ground components. The model integrates the knowledge of urban landscape composition and spectra of remote sensing. The B–D–G model is a fast and effective method to analyze urban landscape composition and its evolution based on remotely sensed images, by employing an explicit endmember evolution implication via the endmember spectrum dynamics. We verify this new method through in situ measurements of spectrum and high resolution images. Then, B–D–G model is used to detect the pattern and types of urban renewal. Despite some limitations, B–D–G model provides a new perspective of modeling urban dynamics and monitoring urban landscape evolution.