x, 99.3% 
S 
5 99.0% 
= 
2 
= 97 À 
€ 0 - SR (8000 clusters) 
2 —e— NBAR (8000 clusters) 
95 T T T 1 
2 3 4 5 
Time Separation Delta-t [months] 
  
Figure 9. Classification accuracies obtained by 
time-domain co-occurrence matrix which is 
defined with 8000 spectral clusters. 
The time-domain co-occurrence matrix defined with 8000 
spectral cluster shows 396-496 higher classification accuracy 
than that of the time-domain co-occurrence matrix defined with 
reflectance. 
MODI2QI and MCDI12Q1 of MODIS land cover product 
which are respectively produced from SR and NBAR products 
showed classification accuracy of 81% and 96%, respectively. 
That is, the classification accuracy obtained by the time-domain 
co-occurrence matrix which is defined with spectral cluster is 
3%-18% higher than that of MODIS land cover products. 
Classification accuracies of MODIS land cover product were 
measured by using same test samples. Because test samples 
were extracted from training area for classification of SR and 
NBAR products, it is fundamentally presumed that the accuracy 
of MOD12Q1 and MCD12Q1 products is lower than that of our 
classification results. However, we consider that these 
classification accuracies of SR and NBAR products showed 
good performance of the proposed simple classification method. 
Figure 10 shows the classification results obtained by cosine 
distance classifier in the case of the highest accuracy. 
5. CONCLUSIONS 
Land cover classification for global scale were performed by 
using two kinds of multi-temporal MODIS reflectance products. 
The proposed method using the time-domain co-occurrence 
matrix and the non-parametric minimum distance classifier 
showed good classification performance compared with 
MOD12Q1 and MCD12Q1 MODIS land cover product. 
The highest classification accuracy was obtained when the non- 
parametric cosine distance classifier was driven by the time- 
domain co-occurrence matrix defined with spectral cluster and 
three or four months time-separation. And also, it was cleared 
that Surface Reflectance 8-Day L3 product and Nadir BRDF- 
Adjusted Reflectance product showed similar classification 
accuracy of 99% for IGBP-17 land cover categories. 
Future study should be carry out in our classification scheme in 
order to examine stability of classification for multiple years 
and to validate classification accuracy with more suitable test 
samples. 
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 
  
  
(a) SR product 
(b) NBAR product 
Figure 10. Land cover classification results. 
Acknowledgements 
This study was supported by the JAXA GCOM-C project under 
contract "JX-PSPC-332027". 
REFERENCES 
Alan Srahler, Doug Muchoney, etc., 1 May 1999. MODIS Land 
Cover Product Algorithm Theoretical Basis Document (Version 
5.0) 
E. F. Vermote and S. Y. Kotchenova, March, 2008. MOD09 
(Surface Reflectance) Users Guide. Land Processes 
DAAC(EROS) in collaboration with the Department of 
Mathematics and Computer Science, South Dakota School of 
Mines and Technology, January 2006. MODIS Reprojection 
Tool User's Manual(Release 3.3a). 
Mikio Maeda, Kiyonari Fukue and Haruhisa Shimoda, 2010. 
Continental scale land cover classification using MODIS 
surface reflectance product. In: Proc. The 31th Asian 
Conference on Remote Sensing, Hanoi, Vietnam, pp.PS01-10-1- 
PS01-10-6.. 
Haruhisa — Shimoda and  Kiyonari  Fukue, 2011. 
CONTINENTAL SCALE LAND COVER CLASSIFICATION 
USING MODIS SURFACE REFLECTANCE PRODUCTS. In: 
2011 IEEE International Geoscience & Remote Sensing 
Symposiums, Vancouver, Canada, pp. 692-695. 
Haruhisa Shimoda and Kiyonari Fukue, 2011. GLOBAL LAND 
COVER CLASSIFICATION USING MODIS SURFACE 
REFLECTANCE PRODUCTS. In: Proc. The 32nd Asian 
Conference on Remote Sensing, TAIPEI, pp.P_241_8-23-20-1-5. 
   
    
        
  
   
    
  
    
    
    
    
   
   
    
  
   
    
      
     
   
   
    
    
   
    
    
   
   
  
   
   
  
  
  
   
  
   
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