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Temporal and spatial dynamics of phenology along the North–South Transect of Northeast Asia

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Vegetation phenology is sensitive to climate change and, as such, is often regarded as an indicator of climate change. It is a common practice to extract vegetation phenological indicators based on satellite remote sensing data. In this study, we used the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Study (GIMMS) Third-Generation normalized difference vegetation index (NDVI3G) to investigate temporal and spatial changes in phenology in Northeast Asia. Based on the maximum rate of change in the NDVI and dynamic threshold, we used the Asymmetric Gaussian model, Double Logistic method, and Savitzky-Golay filter to extract the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), respectively, along the North–South Transect of Northeast Asia (NSTNEA) from 1982 to 2014. We then compared the differences in SOS, EOS, and LOS and considered their spatio-temporal dynamics and relationship with temperature. The results show that the Asymmetric Gaussian model has the highest stability among the three methods. Dynamic thresholds corresponding to the maximum change rate of NDVI were mainly between 0.5 and 0.6. From 1982 to 2014, the SOS in the NSTNEA region occurred approximately 0.19┬ádays earlier each year; the trends in EOS and LOS were not significant. In general, temperature and latitude have a strong linear relationship, both of which significantly impact vegetation phenology in the NSTNEA region. In addition, elevation also significantly impacts on vegetation phenology in the NSTNEA region.
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Document Type: Research Article

Affiliations: 1: Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, China 2: College of Economics and Management, Nanjing Forestry University, Nanjing, China 3: School of Earth Sciences and Engineering, Hohai University, Nanjing, China

Publication date: October 18, 2019

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