Full text: Technical Commission VIII (B8)

    
   
    
    
    
   
     
     
   
    
    
    
   
   
   
    
   
    
   
    
    
    
   
  
  
  
    
   
    
    
   
    
   
   
   
  
   
     
    
  
     
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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 
when K' reached first local maximum during the green- 
up period, and End of Season (EOS) corresponded to 
the time when K' reached last local minima during the 
brown-down period. These moments were considered 
as the transitions of vegetation growing from one lin- 
ear stage to another (Zhang et al, 2003). The DOY 
(Day of Year) of SOS and EOS started from July 1 of 
each year. The length of season was calculated as the 
difference between SOS and EOS. Other phenological 
metrics were calculated using function fitted time se- 
ries. Respectively, Maximum of Greenness (MaxG) and 
Minimum of Greenness (MinG) were maximum and min- 
imum EVI values during a phenological cycle; ampli- 
tude (AMP) was the difference between MaxG and MinG; 
Large Integral of Greenness (LIG) was the daily inte- 
grated EVI value for a phenological cycle; Small Inte- 
gral of Greenness (SIG) was the daily integrated EVI 
during growing season (defined as a period between SOS 
and EOS) subtracted daily integrated MinG during the 
same period. 
2.3.2 Segmented regression to identify breakpoints 
in latitudinal gradients of phenological metrics In 
this study, segmented linear regression was used to iden- 
tify the breakpoint for phenological index along the lat- 
itude. Segmented linear regression applies linear re- 
gression to (xz, y) data that do not have a linear rela- 
tion (Wayne Skaggs, 1996). On the relations between 
phenological metrics and latitude, it is hypothesised that 
there might be a significant breakpoint existed, however, 
generally the relations do not necessarily have to be lin- 
ear. So that with segmented linear regression, the break- 
points can be introduced, among different segments, sep- 
arate linear regression are applied and by this means 
nonlinear relations between phenological metrics and 
latitude might be approximated by a series linear seg- 
ments (Wayne Skaggs, 1996). By calculation of the con- 
fidence intervals of breakpoints, optimum breakpoint, 
which means that the breakpoint with smallest interval 
can be selected (Oosterbaan et al., 1990). 
3 RESULTS AND DISCUSSION 
3.1 Spatial patterns of vegetation phenology 
In the NATT study area, all phenological metrics exhib- 
ited significant spatial patterns in terms of 11 years of 
average conditions (Fig. 2, 3,4, and 5). Over the NATT, 
the dates of vegetation growing season onset (SOS) ranged 
from approximately late August to late January, span- 
ning a five months time period. While the dates of veg- 
etation growing season dormancy (EOS) ranged from 
late February to late October, spanning approximately 
an eight months time period. The length of vegetation 
growing season (LOS), which is the difference between 
EOS and SOS, ranged from about 138 days to 354 days, 
with differences as large as 216 days (7 months). Ta- 
ble 1 and Table 2 provide detailed statistical summary 
for all eight phenological metrics over the whole NATT 
study area. 
The latitudinal gradients of vegetation phenology were 
also very significant in the NATT area. From north to 
Table 1: Five-number summary 1 of phenological met- 
rics in the NATT area. The phenological metrics were 
the average of 11 years. 
SOS EOS LOS  MaxG 
  
  
Minimum 50 243 138 0.1048 
25% quantile 120 355 235 0.1794 
Median 133 3384 298 02395 
75% quantile 143 414 278 02921 
Maximum 214 478 354 0.4902 
  
south, the dates of vegetation growing season onset sig- 
nificantly shifted to later by 3.916 days per latitude de- 
gree (Fig. 6(a)).While the dates of vegetation growing 
season dormancy also shifted south to later by approx- 
imately 1.75 days per latitude degree (Fig. 6(b)), how- 
ever, the latitudinal trend of EOS was not as strong as 
the trend of SOS. The trends of SOS and EOS naturally 
led to the latitudinal trend of LOS (length of season), 
which showed a southward decreasing by approximately 
1.6 days per latitude degree (Fig. 7(a)). The most sig- 
nificant latitudinal gradient came from LIG (integral of 
annual EVI), which can be considered as the vegetation 
annual productivity, showed a almost straight line de- 
cay, where the latitudinal averaged LIG in the south end 
of NATT was only approximately 38% of the north end 
(Fig. 7(b)). 
In the temporal scale, the vegetation phenological met- 
rics in the southern NATT generally had a relatively larger 
interannual variabilities than the northern NATT (the ver- 
tical lines in Fig. 6 and 7, which were the temporal stan- 
dard deviations). 
(a) 
  
-12 
   
  
Y 
(Since July 1) 
Latitude °s 
1 
= 
  
  
  
128 130 Longitude vod 136 138 
Figure 2: Spatial patterns of 11 years (2000-2011) 
mean SOS (the date of growing season onset) in the 
NATT.The numbers in the bracket indicate the calendar 
years, which 1 means first half of phenological year, i.e. 
from July 1 to December 31, 2 means second half of 
phenological year, i.e. from next January 1. Original 
0.05 degree resolution result had been aggregated to 0.2 
degree resolution for plotting purpose. 
3.2 Results of breakpoint analysis 
The breakpoint analysis showed that at least in terms 
of annual minimum EVI (MinG), which was considered 
as a good indicator of tree cover ratio, there was a de- 
tectable change around 18.84 °S and 20.02 °S (Fig. 8),
	        
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