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Open AccessArticle

Trend Evolution of Vegetation Phenology in China during the Period of 1981–2016

by Fusheng Jiao 1,2,3,4, Huiyu Liu 1,2,3,4,*, Xiaojuan Xu 1,2,3,4, Haibo Gong 1,2,3,4 and Zhenshan Lin 1,2,3,4
1
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
2
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China
3
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing Normal University, Nanjing 210023, China
4
College of Geography Science, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 572; https://doi.org/10.3390/rs12030572
Received: 14 November 2019 / Revised: 26 January 2020 / Accepted: 6 February 2020 / Published: 8 February 2020
(This article belongs to the Special Issue Monitoring Vegetation Phenology: Trends and Anomalies)
The trend of vegetation phenology dynamics is of crucial importance for understanding vegetation growth and its responses to climate change. However, it remains unclear how the trends of vegetation phenology changed over the past decades. By analyzing phenology data including start (SOS), end (EOS) and length (LOS) of growth season with the Ensemble empirical mode decomposition (EEMD), we revealed the trend evolution of vegetation phenology in China during 1981-2016. Our study suggests that: (1) On the national scale, with EEMD method, the change rates of SOS and LOS were increasing with time, while that of EOS was decreasing. Moreover, the EEMD rates of SOS and LOS exceeded the linear rates in the early-2000s, while that of EOS dropped below the linear rate in the mid-1980s. (2) For each phenological event, the shifted trends took up a large area (~30%), which was close to the sum of all monotonic trends, but more than any monotonic trend type. The shifted trends mainly occurred in the north-eastern China, eastern Qinghai-Tibetan Plateau, eastern Sichuan Basin, North China Plain and Loess Plateau. (3) For each phenological event, the areas in the high-latitude experienced the contrary trends to the other. The amplitude and frequencies of phenology variation in the mid-latitude were stronger than those in the high-latitude and low-latitude. Meanwhile, LOS in the high-latitude was induced by SOS. (4) For each phenological event, the trend evolution varying with longitudes can be divided into eastern region (east of 121°E), central region (92°E–121°E) and western region (west of 92°E) based on the evolution of trends varying with longitudes. The east experienced a delayed SOS and a shorten LOS, which was different from the other areas. The magnitude of delayed trends in EOS and the prolonged trends in LOS were stronger from east to west as longitudes changes. The variation characteristics of LOS with longitude were mainly caused by SOS in the eastern region and by SOS and EOS together in the western and central region. (5) Each land cover types, except Needleleaf Forests, experienced the same trends. For most land cover types, the advance of SOS, delay of EOS and extension of LOS began in the 1980s, the 1990s, and the 1990s, respectively and enhanced several times. Moreover, the magnitudes of Grasslands in SOS and Evergreen Broadleaf Forest in EOS were much greater than the others, while that of croplands was the smallest in each phenological event. Our results showed that the analysis of trend evolution with nonlinear method is very important to accurately reveal the variation characteristics of phenology trends and to extract the inherent trend shifts. View Full-Text
Keywords: vegetation phenology; trend evolution; EEMD; trend shift; nonlinear vegetation phenology; trend evolution; EEMD; trend shift; nonlinear
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Jiao, F.; Liu, H.; Xu, X.; Gong, H.; Lin, Z. Trend Evolution of Vegetation Phenology in China during the Period of 1981–2016. Remote Sens. 2020, 12, 572.

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