In: Wagner W„ Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
657 
698,000 698500 699p00 699500 700p00 
698000 698500 699000 699500 700000 700500 
II1S1 
Mp'^ 
j rtVÌìfl] wHiÌlEkir 
¿3 • J 3-3 • Egg 
[—] Asphaltic Concrete 
[—I Bigberry Manzanita 
Big Sage brush 
Blue Spruce 
Asphalt 
Concrete 
Decayed Leaf 
|—] Dove Weed 
[—| Grey Bark 
[Ml Grey Leaf 
Unclassified 
Figure 9. Classification results using SAM with maximum angle 
of 0.1 applied to all good VNIR bands. 
Class 
Points 
% 
Area (m 2 ) 
Asphaltic Concrete 
2 
0.020% 
1,800 
Bigberry Manzanita 
549 
5.490% 
494,100 
Big Sage brush 
2,192 
21.920% 
1,972,800 
Blue Spruce 
4 
0.040% 
3,600 
Asphalt 
17 
0.170% 
11,700 
Concrete 
13 
0.130% 
2,700 
Decayed Leaf 
265 
2.650% 
238,500 
Dove Weed 
2,300 
23.000% 
2,070,000 
Grey Bark 
2,317 
23.170% 
2,085,300 
Grey Leaf 
982 
9.820% 
876,600 
Unclassified 
151 
1.510% 
135,900 
Challenges. In Hyperspectral Remote Sensing Of Tropical And 
Subtropical Forests: Edited by Margaret Kalacska and G. 
Arturo Sanchez Azofeifa. CRC Press, Taylor & Francis Group, 
pp. 297-304. 
Gao, B., 1996. NDWI- a normalized difference water index for 
remote sensing of vegetation liquid water from space. Remote 
Sensing of Environment, 58 (3), 257-266. 
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., 
Piper, S. C., Tucker, C. J., et al. (2003, June 6). Climate-driven 
increases in global terrestrial net primary production from 1982 
to 1999, Science, 300, pp. 1560- 1563. 
Richards JA and Xiuping Jia., 2006. Remote Sensing Digital 
Analysis, An Introduction. 4th Edition. Springer Berlin 
Heidelberg New York, pp. 368-373. 
University of Cincinnati, 2003. EO-1 User Guide v. 2.3. 
Obtained from the website: http://eol.usgs.gov & 
http://eol.gsfc.nasa.gov (accessed 20 Dec 2009). 
Table 10. Class Statistics. 
5. CONCLUSION 
From the results above it can be concluded that the spectral data 
of VNIR bands work better than the SWIR, at least for mapping 
land cover of the study area. This means that bands selection is 
very important for matching different objects. Secondly, the 
spectral libraries obtained from the ENVI match with some 
objects in the study area, therefore, there is a need for building 
own spectral library of tropical features. The spectral angle can 
then be tightened for more confident results. 
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