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Predicting phenology using time series of remote sensing data: initial results for the Indian forests

Predicting phenology using time series of remote sensing data: initial results for the Indian forests
Predicting phenology using time series of remote sensing data: initial results for the Indian forests
Time series (2003 to 2007) MERIS Terrestrial Chlorophyll Index (MTCI) products were used to predict the phenology of different forest types in India. The MTCI data were corrected for noise using a temporal moving window filter and then a Fourierbased smoothing was applied without compromising annual phenological cycle. Finally, the phenological variables i.e. onset of greenness and end of senescence, were predicted through iterative search for each pixel using 1.5 years of Fourier smoothed data. Different forest types were extracted from a global land cover map (GLC 2000) and corresponding phenological variables were clipped. Finally, for each forest type, median of phenological variables was derived from four year results and then a spatial majority filter was applied to the 1? x 1? tiles covering complete India. This study presents the initial results derived for the evergreen, semi-evergreen, moist deciduous and dry deciduous forest in India
Jeganathan, C.
f859ef31-fe01-4623-9ebf-d78466c974ca
Dash, J
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Jeganathan, C.
f859ef31-fe01-4623-9ebf-d78466c974ca
Dash, J
51468afb-3d56-4d3a-aace-736b63e9fac8
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Jeganathan, C., Dash, J and Atkinson, P.M. (2009) Predicting phenology using time series of remote sensing data: initial results for the Indian forests. Seminar on Spatial Information Retrieval, Analysis, Reasoning and Modelling. 18 - 20 Mar 2009.

Record type: Conference or Workshop Item (Paper)

Abstract

Time series (2003 to 2007) MERIS Terrestrial Chlorophyll Index (MTCI) products were used to predict the phenology of different forest types in India. The MTCI data were corrected for noise using a temporal moving window filter and then a Fourierbased smoothing was applied without compromising annual phenological cycle. Finally, the phenological variables i.e. onset of greenness and end of senescence, were predicted through iterative search for each pixel using 1.5 years of Fourier smoothed data. Different forest types were extracted from a global land cover map (GLC 2000) and corresponding phenological variables were clipped. Finally, for each forest type, median of phenological variables was derived from four year results and then a spatial majority filter was applied to the 1? x 1? tiles covering complete India. This study presents the initial results derived for the evergreen, semi-evergreen, moist deciduous and dry deciduous forest in India

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More information

Published date: 18 March 2009
Venue - Dates: Seminar on Spatial Information Retrieval, Analysis, Reasoning and Modelling, 2009-03-18 - 2009-03-20

Identifiers

Local EPrints ID: 79704
URI: https://eprints.soton.ac.uk/id/eprint/79704
PURE UUID: 477e323c-5354-4fce-a32d-a56fa6d15ba9
ORCID for J Dash: ORCID iD orcid.org/0000-0002-5444-2109
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

Catalogue record

Date deposited: 19 Mar 2010
Last modified: 18 May 2019 00:38

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