Literature DB >> 10993564

Determining the growing season of land vegetation on the basis of plant phenology and satellite data in Northern China.

X Chen1, Z Tan, M D Schwartz, C Xu.   

Abstract

The objectives of this study are to explore the relationships between plant phenology and satellite-sensor-derived measures of greenness, and to advance a new procedure for determining the growing season of land vegetation at the regional scale. Three phenological stations were selected as sample sites to represent different climatic zones and vegetation types in northern China. The mixed data set consists of occurrence dates of all observed phenophases for 50-70 kinds of trees and shrubs from 1983 to 1988. Using these data, we calculated the cumulative frequency of phenophases in every 5-day period (pentad) throughout each year, and also drew the cumulative frequency distribution curve for all station-years, in order to reveal the typical seasonal characteristics of these plant communities. The growing season was set as the time interval between 5% and 95% of the phenological cumulative frequency. Average lengths of the growing season varied between 188 days in the northern, to 259 days in the southern part of the research region. The beginning and end dates of the surface growing season were then applied each year as time thresholds, to determine the corresponding 10-day peak greenness values from normalized difference vegetation index curves for 8-km2 pixels overlying the phenological stations. Our results show that, at the beginning of the growing season, the largest average greenness value occurs in the southern part, then in the northern, and finally the middle part of the research region. In contrast, at the end of the growing season, the largest average greenness value is measured in the northern part, next in the middle and lastly the southern part of the research region. In future studies, these derived NDVI thresholds can be applied to determine the growing season of similar plant communities at other sites, which lack surface phenological data.

Mesh:

Year:  2000        PMID: 10993564     DOI: 10.1007/s004840000056

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  5 in total

1.  The effects of climate change on the phenology of selected Estonian plant, bird and fish populations.

Authors:  Rein Ahas; Anto Aasa
Journal:  Int J Biometeorol       Date:  2006-09       Impact factor: 3.787

2.  European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modelling?

Authors:  M Migliavacca; E Cremonese; R Colombo; L Busetto; M Galvagno; L Ganis; M Meroni; E Pari; M Rossini; C Siniscalco; U Morra di Cella
Journal:  Int J Biometeorol       Date:  2008-04-24       Impact factor: 3.787

3.  Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland.

Authors:  Shilong Ren; Xiaoqiu Chen; Shuai An
Journal:  Int J Biometeorol       Date:  2016-08-25       Impact factor: 3.787

4.  Comparison of phenology models for predicting the onset of growing season over the Northern Hemisphere.

Authors:  Yang Fu; Haicheng Zhang; Wenjie Dong; Wenping Yuan
Journal:  PLoS One       Date:  2014-10-03       Impact factor: 3.240

5.  Analysis of Differences in Phenology Extracted from the Enhanced Vegetation Index and the Leaf Area Index.

Authors:  Cong Wang; Jing Li; Qinhuo Liu; Bo Zhong; Shanlong Wu; Chuanfu Xia
Journal:  Sensors (Basel)       Date:  2017-08-30       Impact factor: 3.576

  5 in total

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