On a methodology to extract measures of seasonality from MERIS
reduced resolution data to characterise seasonal trends in Irish
vegetation
Brian O’ Connor1, Ned Dwyer1 and Fiona Cawkwell2
1
Coastal & Marine Resources Centre (CMRC), Environmental Research Institute, University
College Cork, Ireland
Email: [email protected]
2
Department of Geography, University College Cork, Cork, Ireland
Summary
Information extracted from satellite data may offer a complementary approach to groundbased methods of monitoring vegetation seasonality (phenology) in Ireland which is currently
limited to a few locations across the country. For this study, ten-day composites from 2003 to
2008 of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), derived
from reduced resolution MERIS Global Vegetation Index (MGVI) data, are used to study
spatio-temporal seasonality trends across the island. Initial work has focused on deriving the
start of growing season (SOS) for ten vegetation classes, representing both natural and
cultivated systems, derived from CORINE 2000 landcover data. The time-series, curve fitting
software, TIMESAT, was used to automatically extract SOS dates for each pixel in each
growing season according to cover type. Applying a fitting function to the MGVI time series
produced a noise-reduced model curve from which seasonality metrics were derived. A
double logistic fit proved the most stable across a number of test pixels of different landcover
types. While no latitudinal or longitudinal gradient is evident from the resulting six-year mean
SOS image, landcover type would appear to be a highly significant factor in determining SOS
although its occurrence is later at higher elevations regardless of landcover.
1
Introduction
Current methods of phenological observation in Ireland are entirely ground-based, at four
International Phenological Garden (IPG) sites, where a number of tree species of common
genetic stock are maintained (Donnelly et al., 2004). However, broad-scale phenological
monitoring is only possible using remote sensing technology. Both approaches are
complementary and therefore can be combined for an integrated approach to phenological
monitoring in Ireland.
The work is based on a growing body of research employing remote sensing methods for
phenological research in the last few decades (Zhang et al., 2001, Reed and Brown, 2005,
Verstraete et al., 2008). Results suggest a greening trend for terrestrial vegetation in certain
parts of Western Europe, attributed to earlier spring warming in line with global climate
change (Xiao and Moody, 2005). In Ireland, there is evidence from phenological gardens that
leaf unfolding now occurs 5 to 8 days earlier than in 1970 leading to an extension in the
growing season (Sweeney et al., 2002).
For this study, a time series of the MERIS MGVI (FAPAR) product has been selected. In a
similar phenological study by Verstraete et al. (2008), seasonality metrics were consistently
derived from a time series of SeaWiFS-derived FAPAR. The reduced resolution (RR) MERIS
FAPAR product (1.2 km) was chosen for this study as it satisfied both requirements of being
a readily available, continuously processed, multiannual time series and at an appropriate
spatial resolution for a national scale study.
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In order to derive metrics from the MGVI time series that are linked to key phenological
events, a model fitting method was used. Model fits allow a per-pixel derivation of the
phenological metrics in a consistent, systematic way which can then be automatically applied
across a grid of pixels. Jönsson and Eklundh’s double logistic and asymmetric Gaussian
function-fitting techniques were selected due to their improved performance over other
modelling techniques (Hird and McDermid, 2008).
2 Aims and Objectives
2.1 Study Objective
The objective of this study is to refine a methodology, using seasonality metrics derived from
a time series of reduced resolution MERIS FAPAR data, to characterise seasonal events, such
as the start of growing season, for a variety of vegetation cover types, based on CORINE
2000 landcover data, across the island of Ireland.
2.2 Study Aims
In order to reach this objective the study has a number of aims:

For the monitoring of island-wide spatio-temporal seasonality trends, daily data over
Ireland must be time-composited over a number of days to attain cloud-free imagery
of the whole island. An aim of this study was to find an appropriate composite period.

The most appropriate model fitting function to derive seasonality metrics from MGVI
time series was explored.

The relation of landcover to any spatio-temporal trends in seasonality was examined.

A method of verification of the derived metric, using the known seasonality of certain
species of trees and plants, was carried out using existing ground data.
3 Methodology
3.1 The generation of a continuous time series of time- composited FAPAR data
The MERIS FAPAR was time-composited with the FAPAR time composite algorithm (Pinty
et al, 2002) in a geographical window over Ireland using the European Space Research
Institute’s (ESRIN) Grid Processing-on-Demand (G-POD) service for the period May, 2002,
to June, 2009.
3.2 The selection of an appropriate composite period
Fieldwork was carried out during the spring months of 2008 and 2009 with the dual purpose
of noting the date of occurrence of key phenological events and tracking the rate of greening
in the vegetation canopy. This helped to guide what an appropriate minimum compositing
period would be to be concurrent with the greening rate. However, to account for daily cloud
cover, an analysis of daily visual cloud observations, from the Armagh Meteorological
Observatory, was conducted.
3.3 Deriving appropriate seasonality metrics from the FAPAR time series
TIMESAT (Jönsson and Eklundh, 2002) was used to compute seasonality metrics from the
smoothed time series. TIMESAT uses least-squares fitting methods to construct local model
fits around maxima and minima in the time series curve before merging the local fits into a
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global fit of the complete time series. The choice between a double logistic and asymmetric
Gaussian basis function is dependent on the nature and extent of noise in the data. A six-year
time series, 2003-2008, of FAPAR data was processed and the CORINE 2000 landcover data
rasterised and grid size resampled to the spatial resolution of MERIS imagery. The SOS
metric was extracted first in order to verify the quality of one metric before extracting other
metrics.
3.4 Verification of the SOS metric using the IPG ground data
The validity of the Start of Season (SOS) metric was verified at one point location by
selecting a ground-based measure, the beginning of leaf unfolding, (BO), recorded at the
Valentia IPG site and comparing it to the SOS metric computed for a pixel corresponding to
the location of the site. The range across the sampled trees, from the earliest to the latest BO
day, of each season was presented along with the SOS metric computed for the Valentia pixel.
4 Results
4.1 TIMESAT Model fitting
Initially, data were fit to a basis of harmonic functions (double logistic) and asymmetric
Gaussian functions, from which seasonality parameters were derived (Jönsson and Eklundh,
2004). Figure 1 illustrates the four steps in fitting a model function to the noisy MGVI time
series in the TIMESAT Graphical Use Interface.
A
B
C
D
Figure 1: Four steps in applying TIMESAT curve fits. (A) Noisy MGVI time series,(B) Noise removal, (C)
Number of seasons calculated, (D) Smooth data series (double logistic fit).
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4.2 TIMESAT output imagery
Figure 2 (a) and (b) show the six-year mean SOS (2003-2008) and the standard deviations of
the mean trend. The resampled CORINE 2000 Landcover is presented in Figure 2 (c). There
are strong indications, based on visual inspection of the results, that landcover and elevation
have significant influence on start dates. However, current work to quantify the influence of
landcover on SOS is being carried out.
(a)
(b)
(c)
Figure 2: (a): The 6-year mean of the SOS metric, 2003-2008. The composite number when SOS occurs is shown on the colour bar, zero
represents areas where no season was calculated or where no data was available, composite ,1 January 1st to 10th and composite 18, June
20th to 29th. (b): the number of standard deviations calculated from the 6-year SOS mean, (c): CORINE 2000 Landcover showing the
landcover classes used.
4.3 Comparison of Beginning of Leaf unfolding (BO) and SOS metric for
Valentia IPG
Table 1 shows the results of the SOS metric for a pixel corresponding to the Valentia IPG as
well as the annual average beginning of leaf unfolding (BO) date recorded at the site for all
trees sampled for the time series, 2003-2008. In each year, the SOS metric falls within the BO
range and the 6-year total mean BO day across all trees is within the 6-year mean SOS.
Table 1. Comparison of the annual range of Beginning of Leaf unfolding (BO) days at the Valentia IPG and
TIMESAT computed metric (SOS) for a pixel corresponding to the same site
2003
2004
2005
2006
2007
2008
6-year
total mean
Valentia IPG (earliest and latest day of leaf 86-112
unfolding)
TIMESAT 10-day SOS (day of year)
76-121
89-131
97-147
70 -117
81-90 121-130 91-100
91-100
91-100
111-120 (101-110)
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5
Discussion
A considerable diversity of landcover is present in the reduced resolution MERIS pixel and
preventing species-level seasonality variation from being discerned. The relation of per-pixel
measures to equivalent ground-measures is also problematic where the seasonality metric
represents the whole vegetation cover rather than any single vegetation species. Averaging or
using a range of point measurements from a number of individual trees over an area
comparable to the size of the MERIS pixel has provided better results.
The selection of the basis function used in TIMESAT modelling, e.g. double logistic or
asymmetric Gaussian, will be subject to a more quantitative evaluation of their performance
rather than a visual assessment of the shape and stability of the modelled curve. Such an
evaluation will be carried out in order to quantify the difference between the modelled and the
original data.
The spatio-temporal patterns in annual SOS dates will be investigated in more detail as will
the six-year trend per landcover type. The CORINE 2006 landcover change database will be
used to assess the influence of landcover changes on the six-year SOS trend. The effect of
spring air temperatures on start dates will also be investigated in a seasonality-climate
correlation study.
In summary, this study has shown that an integrated approach to vegetation seasonality
monitoring, combining medium-spatial resolution satellite data and ground-based
measurements, can offer the potential to show greening trends in Irish vegetation cover. The
processing of a time series of the newly-developed advanced spectral index, MGVI (FAPAR),
in the computer programme, TIMESAT, has provided a method for the automatic and
consistent extraction of seasonality metrics for Ireland over six growing seasons. There is
further potential to improve the TIMESAT outputs by tuning curve fits for each landcover
class. It would appear that a significant pattern has emerged in SOS dates based on landcover
but further work remains to quantify this relationship. Furthermore, a seasonality-climate
correlations study will be carried out to explore the influence of climate on the six-year trend.
6
Acknowledgements
The authors would like to acknowledge the contribution of the EPA (Ireland)-funded Climate
Change Impacts on Phenology (CCIP) project to date as well as the input given by researchers
at Lund University Sweden during time spent there. This project is being funded under the
EPA STRIVE Masters Scholarship.
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