Research Papers

Temporal analysis of urban forest in Beijing using Landsat imagery

[+] Author Affiliations
Chudong Huang, Yun Shao, Jinghui Liu

Institure of Remote Sensing Applications, Chinese Acadamy of Sciences

Jinsong Chen

Chung Chi College, The Chinese University of Hong Kong

J. Appl. Remote Sens. 1(1), 013534 (September 13, 2007). doi:10.1117/1.2794001
History: Received March 14, 2007; Revised August 22, 2007; Accepted August 27, 2007; September 13, 2007; Online September 13, 2007
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Abstract

Urban forest is of great interest to a variety of scientific and urban planning applications. This paper presents a strategy for monitoring urban forest using Landsat TM/ETM+ (Thematic Mapper / Enhanced Thematic Mapper Plus) imagery time series and calculating its ecological benefits. And the strategy is applied to the temporal analysis of the zone inside the 4th Ring Road in Beijing. The analysis consists of two key steps in: the first is to extract urban forest from Landsat images; the second is to calculate the ecological benefits of urban forest. The extraction of urban forest from Landsat imagery is accomplished implementing classification models, which are based on empirical relationships between forest coverage and the spectrum on Landsat imagery, and are generated using regression tree techniques. Quickbird images and field investigations are applied to generate classification models and to assess their accuracies. Subsequently, the ecological benefits calculation about urban forest is carried out introducing CITYgreen model. This paper mainly concerns carbon storage and the function in air pollution reduction. In the following part, the results of the analysis are presented, as well as the figures that illustrate their variations. At the end, the advantages and disadvantages of this strategy are discussed.

© 2007 Society of Photo-Optical Instrumentation Engineers

Citation

Chudong Huang ; Yun Shao ; Jinghui Liu and Jinsong Chen
"Temporal analysis of urban forest in Beijing using Landsat imagery", J. Appl. Remote Sens. 1(1), 013534 (September 13, 2007). ; http://dx.doi.org/10.1117/1.2794001


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