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A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula

Beck, P S A; Jönsson, P; Hogda, K-A; Karlsen, S R; Eklundh, Lars LU and Skidmore, A K (2007) In International Journal of Remote Sensing 28(19). p.4311-4330
Abstract
An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, ( 1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel- specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and ( 2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing... (More)
An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, ( 1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel- specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and ( 2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km x 5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates ( root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
normalized difference vegetation index, remote sensing, time series
in
International Journal of Remote Sensing
volume
28
issue
19
pages
4311 - 4330
publisher
Taylor & Francis
external identifiers
  • wos:000250040500006
  • scopus:34748868405
ISSN
1366-5901
DOI
10.1080/01431160701241936
language
English
LU publication?
yes
id
889b1597-f891-4971-94c2-737150fabc0e (old id 639447)
date added to LUP
2007-12-07 14:44:02
date last changed
2017-08-20 04:35:50
@article{889b1597-f891-4971-94c2-737150fabc0e,
  abstract     = {An NDVI dataset covering Fennoscandia and the Kola peninsula was created for vegetation and climate studies, using Moderate Resolution Imaging Spectroradiometer 16-day maximum value composite data from 2000 to 2005. To create the dataset, ( 1) the influence of the polar night and snow on the NDVI values was removed by replacing NDVI values in winter with a pixel- specific NDVI value representing the NDVI outside the growing season when the pixel is free of snow; and ( 2) yearly NDVI time series were modelled for each pixel using a double logistic function defined by six parameters. Estimates of the onset of spring and the end of autumn were then mapped using the modelled dataset and compared with ground observations of the onset of leafing and the end of leaf fall in birch, respectively. Missing and poor-quality data prevented estimates from being produced for all pixels in the study area. Applying a 5 km x 5 km mean filter increased the number of modelled pixels without decreasing the accuracy of the predictions. The comparison shows good agreement between the modelled and observed dates ( root mean square error = 12 days, n = 108 for spring; root mean square error = 10 days, n = 26, for autumn). Fennoscandia shows a range in the onset of spring of more than 2 months within a single year and locally the onset of spring varies with up to one month between years. The end of autumn varies by one and a half months across the region. While continued validation with ground data is needed, this new dataset facilitates the detailed monitoring of vegetation activity in Fennoscandia and the Kola peninsula.},
  author       = {Beck, P S A and Jönsson, P and Hogda, K-A and Karlsen, S R and Eklundh, Lars and Skidmore, A K},
  issn         = {1366-5901},
  keyword      = {normalized difference vegetation index,remote sensing,time series},
  language     = {eng},
  number       = {19},
  pages        = {4311--4330},
  publisher    = {Taylor & Francis},
  series       = {International Journal of Remote Sensing},
  title        = {A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula},
  url          = {http://dx.doi.org/10.1080/01431160701241936},
  volume       = {28},
  year         = {2007},
}