International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
boundary exists, it might be reflected by vegetation phe-
nology.
In this study, we mainly focused on two points: 1) using
remote sensing to investigate the spatial patterns of veg-
etation phenology in the NATT area; 2) try to identify
the abrupt change (breakpoint) in terms of the phenolog-
ical metrics along the latitude in the NATT, thus provid-
ing a phenological perspective on the biogeographical
boundary question. Our study showed that there were
significant spatial patterns of vegetation phenology in
the NATT during past decades, a north-south directional
trend was identified. Meanwhile, breakpoint analysis
showed that, there was a distinguishable 'breakpoint' lo-
cated around 18 °S to 20°S,
2 DATA AND METHODS
2.1 Study area
4 Howard Spaings A sets.
-15 —
-20 d
-25 -
S
o
-30 -
Latitude
-35 d
7 1 T T
120 130 140 150
Longitude °E
Figure 1: The spatial extent of the NATT study area
(gray colour area)
This study focussed on Northern Australia Tropical Tran-
sect which was extended in this study as a 1.38 million
km? area which located between latitude 12° S and 23°
S and between longitude 128 ° E and 138 ° E (Fig. 1).
The use of transects has been largely adopted by global
change community over past two decades as a standard
method to assess spatial patterns of biogeochemical pro-
cesses (Koch et al., 1995). The spatial variation of long
time constants along the transect can be used as an sur-
rogate of predicted temporal variation to understand the
future responses to global change (Koch et al., 1995).
The Northern Australian Tropical Transect (NATT) was
established under IGBP (International Geosphere Bio-
sphere Programme) in the mid 1990s, and is one of three
transects around world to study global savannas (Koch
et al., 1995). Along the NATT, mean annual precipita-
tion decreases from nearly 1700mm in the north wet end
(Howard Springs) to 300mm in the south dry end (Alice
Springs) (Hutley et al., 2011). The vegetation in NATT
is a wet-dry savannas gradient where in northern half of
NATT, the dominant vegetation is tropical savannas cov-
ered by overstory evergreen Eucalyptus and understory
annual and perennial C4 grasses (Egan and Williams,
1996). However, in southern half of the NATT the dom-
inance of savannas declines, and the dominance of Aca-
cia woodlands and shrub lands and hummock grasslands
increases. (Bowman and Connors, 1996).
2.2 Data
2.2.1 MODI3CI1 EVI A total of more than 11 years
(2000-2011) of MODIS Terra 16-day 0.05 ° spatial reso-
lution collection 5 vegetation indices product (MOD13C1)
were used in this study. This product is mainly de-
signed to provide globally consistent vegetation condi-
tions (Running et al., 1994, Justice and Vermote, 1998).
In this study, the Enhanced Vegetation Index (EVI) was
used as a surrogate for vegetation growth condition. EVI
can effectively reduce the soil background and atmo-
spheric noise while improving the sensitivity in high
biomass regions (Huete et al., 2002). The equation of
EVIis:
Dnir — Pred
EVI=2.5 % 1
Pnir + 6 x Pred — 7.5 X Pblue = 1 ( )
where PNIR, Pred, and ppiue are the wavelengths in the
near infrared, red, and blue bands respectively (Huete et
al., 2002).
The residual cloud and aerosol contamination in the orig-
inal EVI time series were filtered out based on the qual-
ity assurance (QA) flags provided along with the MOD13C1
product.
For pixels without distinct seasonality, such as deserts
and water-bodies, no phenology metrics were derived.
QA filtered data was temporally gap filled by the av-
erage value of the six points before and after the gap.
Remaining noise was removed by a Savitzky-Golay fil-
ter.
2.3 Methods
2.3.1 Phenological metrics retrieval method In this
study, each increase (green-up) and decrease (brown-
down) during a growing season was reproduced by two
separate four parameters logistic function:
b—a
an 1 + exp(==*) e
Y =
where, a is the background EVI before or after growing
season, b is the maximum EVI during growing season, c
is the inflection point when the fitted curve reached the
maximum rising or decreasing speed, d is the scaling
factor which determines the rate of increase or decrease
of EVI at inflection point. The parameters were esti-
mated based on nonlinear least squares criteria for each
pixel and each phenological cycle during 2000-2011, a
total of 11 growth cycles.
The phenological transition dates were determined based
on the curvature change rate K' of the fitted curve fol-
lowed Zhang's method (Zhang et al., 2003). Specifi-
cally, Start of Season (SOS) corresponded to the timing
