Literature DB >> 11769316

An analysis of relationships among plant community phenology and seasonal metrics of Normalized Difference Vegetation Index in the northern part of the monsoon region of China.

X Chen1, C Xu, Z Tan.   

Abstract

This study focuses on relationships between the phenological growing season of plant communities and the seasonal metrics of Normalized Difference Vegetation Index (NDVI) at sample stations and pixels overlying them, and explores the procedure for determining the growing season of terrestrial vegetation at the regional scale, using threshold NDVI values obtained by surface-satellite analysis at individual stations/pixels. The cumulative frequency of phenophases has been calculated for each plant community and each year in order to determine the growing season at the three sample stations from 1982 to 1993. The precise thresholds were arbitrarily set as the dates on which the phenological cumulative frequency reached 5% and 10% (for the beginning) and 90% and 95% (for the end). The beginning and end dates of the growing season were then applied each year as time thresholds, to determine the corresponding 10-day peak greenness values from NDVI curves for 8-km2 pixels overlying the phenological stations. According to a trend analysis, a lengthening of the growing seasons and an increase of the integrated growing season NDVI have been detected in the central part of the research region. The correlation between the beginning dates of the growing season and the corresponding threshold NDVI values is very low, which indicates that the satellite-sensor-derived greenness is independent of the beginning time of the growing season of local plant communities. Other than in spring, the correlation between the end dates of the growing season and the corresponding threshold NDVI values is highly significant. The negative correlation shows that the earlier the growing season terminates, the larger the corresponding threshold NDVI value, and vice versa. In order to estimate the beginning and end dates of the growing season using the threshold NDVI values at sites without phenological data from 1982 to 1993, we calculated the spatial correlation coefficients between NDVI time-series at each sample station and other contiguous sites year by year. The results provide the spatial extrapolation area of the growing season for each sample station. Thus, we can use the threshold NDVI value obtained at one sample station/pixel for a year to determine the growing season at the extrapolation sites with a similar vegetation type for the same year.

Mesh:

Year:  2001        PMID: 11769316     DOI: 10.1007/s004840100102

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


  7 in total

1.  Climatic controls of vegetation vigor in four contrasting forest types of India--evaluation from National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer datasets (1990-2000).

Authors:  V Krishna Prasad; E Anuradha; K V S Badarinath
Journal:  Int J Biometeorol       Date:  2005-05-18       Impact factor: 3.787

2.  A comparative study of satellite and ground-based phenology.

Authors:  S Studer; R Stöckli; C Appenzeller; P L Vidale
Journal:  Int J Biometeorol       Date:  2007-01-18       Impact factor: 3.787

3.  Assessing onset and length of greening period in six vegetation types in Oaxaca, Mexico, using NDVI-precipitation relationships.

Authors:  L Gómez-Mendoza; L Galicia; M L Cuevas-Fernández; V Magaña; G Gómez; J L Palacio-Prieto
Journal:  Int J Biometeorol       Date:  2008-02-26       Impact factor: 3.787

4.  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

5.  Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.

Authors:  Yuanwei Qin; Xiangming Xiao; Jinwei Dong; Yuting Zhou; Zhe Zhu; Geli Zhang; Guoming Du; Cui Jin; Weili Kou; Jie Wang; Xiangping Li
Journal:  ISPRS J Photogramm Remote Sens       Date:  2015-05-04       Impact factor: 8.979

6.  Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

Authors:  Fabio Fontana; Christian Rixen; Tobias Jonas; Gabriel Aberegg; Stefan Wunderle
Journal:  Sensors (Basel)       Date:  2008-04-23       Impact factor: 3.576

7.  Widespread climate change in the Himalayas and associated changes in local ecosystems.

Authors:  Uttam Babu Shrestha; Shiva Gautam; Kamaljit S Bawa
Journal:  PLoS One       Date:  2012-05-15       Impact factor: 3.240

  7 in total

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