Monitoring urban greenness dynamics using multiple endmember spectral mixture analysis.
Urban greenness is increasingly recognized as an essential constituent of the urban environment and can provide a range of services and enhance residents' quality of life. Understanding the pattern of urban greenness and exploring its spatiotemporal dynamics would contribute valuable information for urban planning. In this paper, we investigated the pattern of urban greenness in Hangzhou, China, over the past two decades using time series Landsat-5 TM data obtained in 1990, 2002, and 2010. Multiple endmember spectral mixture analysis was used to derive vegetation cover fractions at the subpixel level. An RGB-vegetation fraction model, change intensity analysis and the concentric technique were integrated to reveal the detailed, spatial characteristics and the overall pattern of change in the vegetation cover fraction. Our results demonstrated the ability of multiple endmember spectral mixture analysis to accurately model the vegetation cover fraction in pixels despite the complex spectral confusion of different land cover types. The integration of multiple techniques revealed various changing patterns in urban greenness in this region. The overall vegetation cover has exhibited a drastic decrease over the past two decades, while no significant change occurred in the scenic spots that were studied. Meanwhile, a remarkable recovery of greenness was observed in the existing urban area. The increasing coverage of small green patches has played a vital role in the recovery of urban greenness. These changing patterns were more obvious during the period from 2002 to 2010 than from 1990 to 2002, and they revealed the combined effects of rapid urbanization and greening policies. This work demonstrates the usefulness of time series of vegetation cover fractions for conducting accurate and in-depth studies of the long-term trajectories of urban greenness to obtain meaningful information for sustainable urban development.
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China.
Muye
M
Gan
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China; School of Civil Engineering and Environmental Sciences and School of Meteorology, University of Oklahoma, Norman, Oklahoma, United States of America.
Jinsong
J
Deng
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China.
Xinyu
X
Zheng
School of Civil Engineering and Environmental Sciences and School of Meteorology, University of Oklahoma, Norman, Oklahoma, United States of America; State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China.
Yang
Y
Hong
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang Province, China.
Ke
K
Wang
D002681:China; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D040362:Geographic Information Systems; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010944:Plants; D058998:Remote Sensing Technology / Q000379:methods*; D062211:Spatio-Temporal Analysis; D014507:Urbanization