International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
ANOVA df SS MS
Regression 1 38189791 38189792
Residual 1312 107881906 82227.06
Total 1313 146071698
F= 464.4 Significance F 1.96E-88
Table 7. ANOVA of the linear regression between mean
gradient and mean elevation of the barren terrain class objects.
2000 4————————————9———— ——————————
T 160ü a ; -
* ++ + } .
m 1200 PA pr EN |
e ro + pere $e. ^ 4 4T eM . |
o 800 t, uw. ESS Ny =: Ste AT |
= * e Mt rta POM Rios. —o*t, pi t 2 e |
= 2, {SHENG Fe de ac TAS IH * |
400 - Ares LU fry a AF anum Aen uet |
gi he Trl ag: ny ; |
0 5 10 15 20 25 30 35 40 45
Mean G
Figure 8. Linear regression line and scatter diagram.
The objects wer sliced to 5 classes in increasing mean gradient
and mapped (Figure 9).
Figure 9. Gradient classes.
3. CONCLUSSION
The geomorphometric description of the cluter map indicated
that the major landcover classes (forest versus cultivate land
versus bare ground) present specific and distinct parametric
representation. The barren class presents high elevation and
gradient while the correlation and the linear regression
inidicated that the greatest in size objects occupy the greatest in
height position and present the greatest gradient. This finding
should be taken into account in the urban planning since barren
class object are distribute along the major highway that
connects the major city with the major port of this island
(Kefalonia).
4. REFERENCES
l. Abrams, M. Hook S., 2002. ASTER user handbook
version 2. Jet Propulsion Laboratory, 135 p.
2. ASTER 2003. ASTER WEB PAGE.
http://asterweb.jpl.nasa.gov/ (accessed 18 Jan. 2003).
3. Chavez, P, 1996. Image-based atmospheric corrections
revisited and revised. Photogrammetric Engineering and
Remote Sensing, 62, pp. 1025-1036.
4. Eastman J. 1999. Idrisi 32: guide to GIS and image
processing, volume 2. Clark Labs, Worcester, 169 p.
Elumnoh A., Shrestha R., 2000. Application of digital
elevation data to Landsat image classification.
Photogrammetric Engineering & Remote Sensing, pp. 297-
304
6. Evans, LS., 1980. An integrated system for terrain analysis
and slope mapping. Zeitschrift fuer Geomorphologie N.F.
Suppl.-Bd., 36, pp. 274-290.
7. Florinsky I., 1998. Accuracy of local topographic variables
derived from digital elevation models. Int. J. of
Geographical Information Science, 12, pp. 47-61.
8. Fujisada, H., 1998. ASTER Level 1 data processing
algorithm. /EEE Transactions on Geoscience & Remote
Sensing, 36, pp. 1101-1112.
9. Kahle, A, et al., 1991. The advanced spaceborne thermal
emission & reflectance radiometer (ASTER). /nternational
Journal of Imaging Systems & Technology, 3, pp.144 -156.
10. Mark, D., 1975. Geomorphometric parameters: a review
and evaluation. Geographiska Annaler, 57A(10), pp. 1461-
1467.
11. Mather, P., 1987. Computer processing of remotely-sensed
images. John Wiley & Son, New York, 352 p.
12. Miliaresis, G., 1999a. A region-growing algorithm for the
segmentation of alluvial fans from
wh
digital elevation
models. 1” Imaging
Applications in Geology (GeoVision 99), Liege, Belgium,
6-7 May, pp. 189-192.
13. Miliaresis, G., 1999b. Automated Segmentation of
Alluvial Fans to Regions of High to Intermediate Flood
Hazard from Landsat Thematic Mapper Imagery. 2nd Int.
Symposium Operationalization of Remote Sensing, ITC
Enschede, 16-20 August., 6 pages.
14. Miliaresis, G., 2001. Extraction of bajadas from digital
elevation models and satellite imagery. Computers &
Geosciences, 27, pp.1159-1169.
Panagou Th, Miliaresis G., 2003. Urban landcover
classification by the use of ASTER imagery. 4th Int.
Symposium of Remote Sensing of Urban, Areas, 27-29
June, Regensberg (Germany), 6 pages.
16. Pike R., 2002. A bibliography of terrain modeling
(Geomorphometry), the quantitative representation of
topography, supplement 4.0. US Geological Survey, Open-
File-Report 02-465, Menlo Park, CA, 157 p.
17. Treitz P., Howarth P., 2000. Integrating spectral, spatial
and terrain variables for forest ecosystem classification.
Photogrammetric Engineering & Remote Sensing, 66, pp.
305-317.
18. White, K., 1993. Assessing flood hazards on dryland
piedmonts using Landsat Thematic Mapper data.
Proceedings of the Remote Sensing Society, Chester
College, United Kingdom, September 16-17, 8 pages
International] Symposium on
un
4.1 Acknowledgements and Appendix (optional)
Acknowledgements of US Geological Survey for providing the
A.S.T.E.R image used in this research effort.
