Journal of Geographical Sciences ›› 2019, Vol. 29 ›› Issue (1): 131-145.

• Research Articles •

A comprehensive analysis of phenological changes in forest vegetation of the Funiu Mountains, China

Wenbo ZHU(), Xiaodong ZHANG, Jingjing ZHANG, Lianqi ZHU

1. College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
• Received:2018-04-17 Accepted:2018-06-20 Online:2019-01-25 Published:2019-03-15
• About author:

Author: Zhu Wenbo (1989-), PhD, specialized in the mountain ecosystem service, development and utilization of regional natural resources. E-mail: zhuwb517@163.com

• Supported by:
National Natural Science Foundation of China, No.41671090;National Basic Research Program (973 Program), No.2015CB452702

Abstract:

This paper reports the phenological response of forest vegetation to climate change (changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series images from 2000 to 2015. The phenological parameters of forest vegetation in the Funiu Mountains during this period were determined from the temperature and precipitation data using the Savitzky-Golay filter method, dynamic threshold method, Mann-Kendall trend test, the Theil-Sen estimator, ANUSPLIN interpolation and correlation analyses. The results are summarized as follows: (1) The start of the growing season (SOS) of the forest vegetation mainly concentrated in day of year (DOY) 105-120, the end of the growing season (EOS) concentrated in DOY 285-315, and the growing season length (GSL) ranged between 165 and 195 days. There is an evident correlation between forest phenology and altitude. With increasing altitude, the SOS, EOS and GSL presented a significant delayed, advanced and shortening trend, respectively. (2) Both SOS and EOS of the forest vegetation displayed the delayed trend, the delayed pixels accounted for 76.57% and 83.81% of the total, respectively. The GSL of the forest vegetation was lengthened, and the lengthened pixels accounted for 61.21% of the total. The change in GSL was mainly caused by the decrease in spring temperature in the region. (3) The SOS of the forest vegetation was significantly partially correlated with the monthly average temperature in March, with most correlations being negative; that is, the delay in SOS was mainly attributed to the temperature decrease in March. The EOS was significantly partially correlated with precipitation in September, with most correlations being positive; that is, the EOS was clearly delayed with increasing precipitation in September. The GSL of the forest vegetation was influenced by both temperature and precipitation throughout the growing season. For most regions, GSL was most closely related to the monthly average temperature and precipitation in August.