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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/639447
- author
- Beck, P S A
; Jönsson, P
; Hogda, K-A
; Karlsen, S R
; Eklundh, Lars
LU
and Skidmore, A K
- organization
- publishing date
- 2007
- 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
- 2016-04-04 07:12:24
- date last changed
- 2022-03-15 06:42:06
@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}},
keywords = {{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}},
doi = {{10.1080/01431160701241936}},
volume = {{28}},
year = {{2007}},
}