Vegetation phenology mapping of Wuhan and analysis of the affecting factors
CHEN Ke1， LI Xinghua2， GUAN Xiaobin1， SHEN Huanfeng1
1.School of Resource and Environmental Sciences， Wuhan University， Wuhan 430079， China；2.School of Remote Sensing and Information Engineering， Wuhan University， Wuhan 430079， China
Abstract：As the indicator of responses of vegetation growth to climate changes， phenology is of much significance in studying climate changes and urbanization. As the distributions of vegetation inside urban areas are usually scattered， low-resolution Remote Sensing images of the kilometer level hardly recognize vegetation and conduct analyses well， while the Remote Sensing data of 10m spatial resolution are difficult to meet the requirements of phenological analysis in terms of time resolution. To solve the above problem， a spatial and temporal fusion method of Remote Sensing data is applied in this paper to alleviate the contradiction between time resolution and spatial resolution， and phenological mapping together with analysis of affecting factors are carried out. Based on the non-local means filter fusion method， the surface reflectance and EVI (Enhanced Vegetation Index) time series with spatial resolution of 30 m and time resolution of 8 d in Wuhan are generated. The EVI time series are reconstructed by Moving Weighted Harmonic Analysis， and vegetation phenology of Wuhan from 2006 to 2014 are extracted by dynamic threshold method. Through analysis， it is found that： 1) from 2006 to 2014， the vegetation phenology of Wuhan from urban to suburb shows the spatial distribution pattern of the gradual delay of the start of season (SOS)， the gradual advance of the end of season (EOS)， and the extension of the growing period (LOS)， with the overall trend of SOS advancement， EOS delay， and LOS extension observed on temporal scales； 2) the correlation between vegetation phenology and average temperature is not significant， but EOS and LOS are significantly affected by the annual average daily temperature amplitude. For an average daily amplitude of 1°C， EOS is delayed by about 12 d， and LOS is extended by about 16 d. Precipitation mainly affects SOS and LOS. For every 100 mm increase in precipitation， SOS is about 5 d ahead of schedule， and LOS is extended for about 9 d.
2019, Vol. 53 
