Responses of the net primary productivity of vegetation to phenological changes in Beijing of China

被引：0
|
作者：
Wei X. [1 ,2 ]
Gao Y. [3 ]
Fan Y. [4 ]
Lin L. [3 ]
Mao J. [1 ,2 ]
Zhang D. [5 ]
Li X. [1 ,2 ]
Liu X. [1 ,2 ]
Xu M. [1 ,2 ]
Tian Y. [1 ,2 ]
Liu P. [1 ,2 ]
Jia X. [1 ,2 ]
Zha T. [1 ,2 ]
机构：
[1] School of Soil and Water Conservation, Beijing Forestry University, Beijing
[2] Key Laboratory of State Forestry Administration on Soil and Water Conservation, Beijing Forestry University, Beijing
[3] Planning and Monitoring Center of Beijing Forestry and Landscape, Beijing
[4] The Department of Management for Songshan National Reserve, Beijing
[5] Miyun District Forestry and Parks Bureau, Beijing
来源：
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | 2022年 / 38卷 / 18期
关键词：
climate change; NDVI; net primary productivity; remote sensing; vegetation phenology;
D O I：
10.11975/j.issn.1002-6819.2022.18.018
中图分类号：
学科分类号：
摘要：
Net Primary Productivity (NPP) of vegetation is considered one of the main indicators for the carbon fixation capacity of ecosystems in the carbon cycle, particularly for the adaptability of ecosystems to climate change. Among them, the typical phenological factors are the key components of the ecosystem functions in many biological processes, including the Start of the Growing Season (SOS), End of the Growing Season (EOS), and Length of the Growing Season (LOS). However, it is still lacking in the relative importance of phenological and climatic factors to the NPP. The contribution of phenological factors (SOS, EOS, and LOS) to the interannual change of NPP has not been well quantified, due to the complex ecosystem. Therefore, this study aims to extract the phenological information of vegetation using a Cardiovascular-Ames-Stanford Approach (CASA) model, in order to examine the characteristics of spatial and temporal changes of NPP. The Normalized Vegetation Index (NDVI) was used from the MODIS data in Beijing from 2001 to 2020. The interaction between meteorological factors, phenological changes, and NPP was then explored using linear regression, trend analysis, and structural equation modeling. The results show that the SOS was gradually advanced by 0.57 each year on average from 2001 to 2020 over more than 70% of the regions, whereas, the EOS was gradually postponed by an average of 0.51 days per year over more than 90% of the regions. The NPP vegetation increased significantly from 2001 to 2020 (P < 0.05), where the growth rate was greater in the last 10 years than that in the first 10 years. The average annual NPP value was greater in the northern region than that in the southern. There was a strong correlation between the phenological factors (SOS, and LOS) and NPP (P<0.05). The pixel-by-pixel analysis also found that the SOS, LOS, and NPP were significantly correlated in the areas with more than 75% vegetation coverage. The NPP was also significantly affected by the advance of SOS and extension of LOS (P<0.05). The NPP increased by 3.74 g/m2 for every single day advance of SOS, while by 2.65 g/m2 for every single day extension of LOS. There was no significant effect of the EOS delay in autumn on the NPP. A coupling effect of phenology and climatic factors varied with the season. There was a greater indirect effect of climate through changing phenology (SOS and EOS) on the NPP in spring and autumn, compared with the direct. By contrast, the NPP was more directly affected by climatic factors, temperature, and rainfall in summer. In conclusion, the spring phenological change was an important driving factor for the interannual variation in the NPP. Furthermore, the annual NPP increased to the phenological advance. The findings can also provide an important supplement to determine the vegetation productivity response to the climate change in urban areas. © 2022 Chinese Society of Agricultural Engineering. All rights reserved.
引用
收藏
页码：167 / 175
页数：8
相关论文
共 37 条
  • [21] Li Guicai, Estimation of Chinese Terrestiral Net Pirmary Production Using LUE Model and MODIS data, (2004)
  • [22] Chen J, Jonsson P, Tamura M, Et al., A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J], Remote Sensing of Environment, 91, 3, pp. 332-344, (2004)
  • [23] Xu Ke, Study on Growing Season and Its Influencing Factors in Beijing based on MODIS Images, pp. 1-55, (2012)
  • [24] Liu Zhenzhen, Zhang Xiwang, Chen Yunsheng, Et al., Remote sensing estimation of biomass in winter wheat based on CASA model at region scale, Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 33, 4, pp. 225-233, (2017)
  • [25] Potter C S, Randerson J T, Field C B, Et al., Terrestrial ecosystem production: A process model based on global satellite and surface data, Global Biogeochemical Cycles, 7, 4, pp. 811-841, (1993)
  • [26] Theil H., A rank-invariant method of linear and polynomial regression analysis, 53, pp. 386-392, (1950)
  • [27] Sen PK., Estimates of the regression coefficient based on Kendall’s Tau, Journal of the American Statistical Association, 63, 324, pp. 1379-1389, (1968)
  • [28] Qiao Ying, Ma Yingjie, Xin Mingliang, Analysis of evapotranspiration characteristics of ziziphus jujuba Mill. Orchards in arid areas based on improved S-W and structural equation model, Transactions of the Chinese Society for Agricultural Machinery, 52, 8, pp. 307-317, (2021)
  • [29] Bao G, Chen J, Chopping M, Et al., Dynamics of net primary productivity on the Mongolian Plateau: Joint regulations of phenology and drought, International Journal of Applied Earth Observation and Geoinformation, 81, pp. 85-97, (2019)
  • [30] Wang X F, Xiao J F, Li X, Et al., No trends in spring and autumn phenology during the global warming hiatus, Nature Communications, 10, (2019)
1234