The Dynamic Of Urban Landscape Phenology In Shanghai And Its Influence Factors

Previous studies revealed that plant phenology in urban area advanced that in surrounding rural area due to urban heat island (UHI) effect. Urban landscape structure influences spatial pattern of UHI. However, how plant phenology varies along urban-rural gradient? And what’re the correlative factors of these variations of plant phenology are still unclear. The objective of this study is to find out the spatial and temporal variation of urban vegetation phenology along the urban to rural gradient in Shanghai and the underlying factors which correlate to these changes. The results of our study will be helpful to better understand how urbanization influences landscape phenology in urban areas.High spatial resolution aerial photos and Landsat images were used to derive land use land cover data and to construct time series information of key phenophases, i.e. the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) of evergreen and deciduous vegetation in Shanghai. Landscape metrics were calculated using land use and land cover data to characterize urban landscape pattern. Spearman’s correlation analysis is utilized to identify the driving factors to these alterations of plant phenology. And the result was validated against field observation data of 2013 and 2014. The main conclusions are as follows:1. The Landsat images data from the same sensor of Landsat can be used to fit double logistic (Double-logistic) curve. Phenophase parameters obtained can effectively reflect the historical variations of vegetation phenology in the urban areas. The SOS of plant phenology advanced gradually over time, the EOS delayed, therefore, the LOS was extended.2. The Landsat images from different sensors cannot be used to construct the time series of vegetation indices, since the parameters are different. The quality of Landsat 8 images has greatly improved compared with the previous Landsat images such as Landsat TM or ETM+. The sensor of Landsat 8 is more sensitive to vegetation change and therefore more accurate to drive ground information of plant phenology.3. There were significant delay of SOS for evergreen and deciduous vegetation along the urban to rural gradient in two periods of 2004-2009 and 2013-2015. The EOS of these two types of vegetation advanced with the distance from urban center in 2013-2015. The LOSs were shorten in the two periods of 2004-2009 and 2013-2015 due to the change of SOS and EOS. The change rate indicated by the slope of linear regression of EOS and LOS of the two vegetation types increased rapidly during the two periods (from2004-2009 to 2013-2015).4. The correlation coefficient between landscape metrics and each phenophses demonstrated that landscape pattern could influence urban landscape phenology, especially the phenophases of EOS and LOS of deciduous plant and EOS of the evergreen.5. Deciduous plant phenology information extracted by Remote Sensing can predict the synchronization leaf unfolding of Salix babylonica. As for the leaf unfolding of Platanus hispanica, the Remote Sensing approach can predict 40 days in advance. The prediction errors for these two species are less than ten days. For the other deciduous species, the phenophase information derived using remote sensing method is not concordant with the in situ observations of phenology.