The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China's Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001-2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann-Kendall nonparametric test and Sen's slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001-2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001-2015 (-0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China.
CITATION STYLE
Yao, R., Wang, L., Huang, X., Guo, X., Niu, Z., & Liu, H. (2017). Investigation of urbanization effects on land surface phenology in Northeast China during 2001-2015. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010066
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