Full text: Proceedings, XXth congress (Part 7)

multi- 
for the 
values. 
ion or 
'opped 
uld be 
nd the 
: cloud 
on and 
ection, 
stically 
IS data 
'82 
    
     
VIODIS 
AVHRR 
« 
by TM 
  
onal to 
.data by 
ustering 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
(Pan et al., 2003; USGS - NASA Distributed Active Archive 
Center, 2004a), maximum likelihood classification (Liu et 
al, 2003), decision tree classification (Townshend et al., 
1999), and hybrid algorithm of decision tree and neural 
network (Friedl et al, 2002). Running et al. proposed a 
simple new logic for classifying global vegetation based on 
the structural characteristics of vegetation (Running et al., 
1995). Since the classification result in this study is the 
input parameter for the hydrological model, the critical 
feature of the classification method is to control the 
classification result using simple parameters. 
2.2 Data used 
MODIS products are being used by scientists from a variety 
of disciplines, including oceanography, biology, and 
atmospheric science. Most users will obtain data products 
by ordering them through an ordering system (Conboy). The 
product named "Surface Reflectance 8-Day L3 Global 250m" 
(abbreviated as MODO09QI) is used in this study. MODIS 
250 m Surface Reflectance is a two-band product computed 
from the MODIS Level 1B land bands | and 2 (centered at 
648 nm and 858 nm, respectively). The product is an 
estimate of the surface spectral reflectance for each band as it 
would be measured at ground level if there were no 
atmospheric scattering or absorption (USGS - NASA 
Distributed Active Archive Center, 2004b). The 45 periods 
of MOD09QI products in the whole of 2002 are re-projected 
to the Equirectangular (latitude/longitude) projection by 
means of MODIS Reprojection Tool (USGS - NASA 
Distributed Active Archive Center, 2004c). The spatial 
coverage is from 50 degrees north to 20 degrees north in 
latitude, and 90 degrees east to 150 degrees east in 
longitude, and the resolution is 7.5 arc seconds. The 
following multi-temporal metrics are derived from the time 
series: 
1. Annual maximum NDVI (NDVI ann max) 
2. Annual minimum NDVI (NDVI ann min) 
3. Annual amplitude of NDVI (NDVI ann amp) 
4. Annual minimum band 1 reflectance (Refl ann min) 
5. Annual minimum band 2 reflectance (Ref2 ann min) 
6. April minimum NDVI (NDVI apr min) 
7. June maximum NDVI (NDVI jun max) 
8. August minimum NDVI (NDVI aug min) 
Annual maximum/minimum of NDVI/reflectance (1,2,4, and 
5) are the *quasi" maximum/minimum value, that is, the 
second maximum/minimum value in the 22 periods from 
April 23 to October 8 is selected for the purpose of avoiding 
the snow and erroneous data. Annual amplitude of NDVI is 
derived from NDVI ann max - NDVI ann min. April 
minimum, June maximum, August minimum NDVI is pure 
maximum or minimum value in the period from Apr. 7 to 
May 9, Jun. 2 to Jul. 4, and Aug. 5 to Sep. 6, respectively. 
2.3 Algorithm 
The supervised decision tree classification method is 
selected in this study for the reason of the easiness in 
controlling of the classification result. The decision tree 
classification algorithms has significant potential for land 
cover mapping problems (Friedl et al. 1997), and its 
performance is acceptably good in comparison with that of 
837 
other classifiers, except with high-dimensional data (Pal et 
al., 2003). The scheme of the classification method in this 
study is shown in Figure 2. The eight threshold values are 
arranged in the each steps of the decision tree, and the result 
of the land cover classification is tuned by these threshold 
values. Ref2 ann min is applied to extract water area since 
the spectral reflectance of water in near-infrared wavelength 
band is much lower than that of land surface. 
NDVI ann max represent the most active status of the 
vegetation in the year, hence it is applied to categorize the 
non-vegetation and less-vegetation from other vegetated 
land. NDVI ann min is applied to the vegetated area for the 
discrimination between evergreen and deciduous vegetation. 
Refl ann min is also applied to the vegetated area to 
discriminate tree type and grass type vegetation based on 
the rough assumption that the tree type land surface is 
generally “darker” in visible wavelength as against grass 
type land. The time series of NDVI apr min, NDVI jun max, 
and NDVI aug min are jointly used to extract double 
cropped agricultural area especially in the downstream of 
Yellow River basin. The first cropping season is from middle 
of February to end of May, and second season is from July to 
middle of November in this region. This phenological 
characteristic is unique compared with other natural 
grasslands or single cropped agricultural fields. Therefore, if 
NDVI apr min is greater than NDVI jun max and 
NDVI jun max is less than NDVI aug min, that pixel is 
categorized as double cropped agricultural field. 
  
  
Réf2 ann min. 
  
  
  
+ True 
Water >< Threshold : False 
d Ce Threshold 1: 0.03 
NDVI ann max Threshold 2: 0.11 
Non-vege > 
Threshold 3: 0.375 
Threshold 4: 0.375 
Threshold 5: 0.029 
Threshold 6: 0.3 
Threshold 7: 0.5 
Threshold 8: 0.026 
  
| <Threshold2” 
  
NDVI ann max 
Less-vege . « Threshold3 7 
  
  
  
NDVI ann in in 
< 
  
  
  
  
  
  
  
  
  
  
  
^w. Threshold4^ 
YN, AP mb 
| Evergreen | | Deciduous | 
Réfl ann min Réf] ann min, 
^x Threshold8^ "«Threshold5^ 
Forest Grassland Forest | Grass type | 
A 
-” 
NDVI anfi_amp » Thresholdé NN 
NDVI_ann_max > Threshold? ae time Senes 
Y M 
Agri. (double) 
  
  
Grassland Agri. (single) 
  
  
  
  
  
  
  
  
Figure 2. Flow of the land cover classification 
 
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.