Acta Geographica Sinica ›› 2019, Vol. 74 ›› Issue (3): 490-503.doi: 10.11821/dlxb201903007

• Climate Change and Surface Process • Previous Articles     Next Articles

Monte Carlo survival analysis on the influencing factors of forest phenology in Northeast China

Li ZHUO(), Ziyan ZHANG, Xiaoyu LEI, Qiuping LI, Haiyan TAO()   

  1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2017-08-02 Revised:2018-12-23 Online:2019-03-25 Published:2019-03-19
  • Supported by:
    National Science Foundation of China, No.41371499


Quantitative analysis of the influencing factors of vegetation phenology is not only helpful to accurate assessment of impacts of climate change on vegetation but also has great importance in the improvement of regional climate models, as well as accurate estimation of vegetation net primary productivity and carbon balance. Vegetation phenology monitoring based on remote sensing data has made great progress, however, few studies have focused on analyses of the influencing factors of vegetation phenology based on large-scale and time series remote sensing data. The use of the linear regression model in some existing studies has certain limitations due to the nonlinearity of vegetation phenology. In this paper, we propose a Monte Carlo based survival analysis method, which was applied to the forest regions of Northeast China. Start of season (SOS), end of season (EOS) and growing season length (GSL) were firstly extracted from time series AVHRR GIMMS NDVI data of the study area in the period of 1982-2009, using the double logistic curve fitting method. And then the survival analysis model of vegetation phenological influencing factors based on Monte Carlo estimation was constructed. Finally, the proposed method was applied to the forest regions in Northeast China to investigate possible influencing factors of vegetation phenology in the rejuvenation period and deciduous period. Results show that temperature, precipitation, and wind can influence phenology of the forest in the region, with temperature being the primary influencing factor for both start and end of seasons. Long-term changes of average temperature have more significant impacts on the forest phenology, compared with short-term temperature variations. The increase of wind speed before the EOS may lead to an early EOS. In addition to environmental factors, EOS tends to be later if SOS is early. The results also prove that the proposed survival analysis method can provide a good scheme to quantitatively analyze the influencing factors of the phenological periods.

Key words: survival analysis, Monte Carlo, phenology, Northeast Forest Region, Response