International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
prises unt
Unchanged
c: Hash Baishfness
High Greenness
High Wetstess
Media Briditness
Medii: Or eeunesá
S Medium Wetaess
Figure 6. ISODATA classification image
In Figure 6 each class indicates intensity of vector change in
any direction within the Tasseled Cap Transformation.
Class Value Area (ha) %
0 No change 24716.44 86.51
1 High wetness 350.12 1.22
2 High greenness 650.88 2.28
3 High brightness 1282.20 4.49
4 Medium wetness 131.20 0.46
5 Medium greenness 1296.60 4.54
6 Medium brightness 142.56 0.50
Table 3. Landscape dynamic classes and their corresponding
area in the Terkos
After image classification, accuracy of the resultant image was
assessed. Accuracy assessment is very important for
understanding the detected change results and employing these
results for land management, urban land planning and decision-
making (Foody, 2002). In this study, overall accuracy and
Kappa analysis were used to perform classification accuracy
assessment based on error matrix. Overall accuracy and Kappa
statistics were calculated as 84.32 % and 0.81, respectively.
5. CONCLUSIONS
In this study, mCVA used to detect the most dynamic areas in
Terkos Water Basin, Istanbul. Three TC features (brightness,
greenness and wetness) were derived for SPOT 5 MS data to
produce three difference, one magnitude and three direction
images using 2003 dated SPOT 4 and 2007 dated SPOT 5 MS
data. Classified vector magnitude and direction components
were used for the selection of dynamic landscapes. The
advantage of the mCVA is it has the ability to process any
number of spectral bands but it is difficult to decide changed
classes and threshold value. The sensitivity of the CVA was
enhanced by using polar coordinates to represent vector
directions. CVA and extended polar coordinates improved the
ability to determine landscape dynamics in this heterogeneous
environment.
ACKNOWLEDGMENT
The authors would like to thank the SPOT OASIS project for
providing the 2007 dated SPOT 5 MS and PAN images.
REFERENCES
Balik Sanli F., Bektas Balçik F., and Goksel C., 2007. Defining
temporal spatial patterns of mega city Istanbul to see the
impacts of increasing population. Environmental Monitoring
and Assessment, 146, 1-3, pp. 267-275.
Bektas Balçik, F., 2010. Mapping and Monitoring Wetland
Environment by Analysis of Different Satellite Images and Field
Spectroscopy’. Istanbul Technical University, PhD thesis.
Chavez, P. S., 1988. An improved dark-object subtraction
technique for atmospheric scattering correction of multispectral
data. Remote Sensing of Environment, 24, pp. 459—479.
Chen, J., Gong, P., He, C. and Shi, P., 2003. Land-use/land-
cover change detection using improved change-vector analysis.
Photogrammetric Engineering and Remote Sensing, 69, pp.
379.
Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E.,
2004. Digital change detection methods in ecosystem
monitoring; a review. International Journal of Remote Sensing,
25, pp. 1565-1596.
Coskun, H. G., Gulergun, O., Yilmaz, L., 2006. Monitoring of
protected bands of Terkos drinking water reservoir of
metropolitan ‘Istanbul near the Black Sea coast using satellite
data. International Journal of Applied Earth Observation and
Geoinformation, 8, pp. 49-60.
Crist, E.P., and Kauth, R. J., 1986. The Tasseled Cap de-
mystified. Photogrammetric Engineering and Remote Sensing,
52, pp. 81-86.
Flores, S. E., Yool, S. R., 2007. Sensitivity of change vector
analysis to land cover change in an arid ecosystem.
International Journal of Remote Sensing, 28, pp. 1069-1088.
Foody, G. M., 2002: Status of land cover classification accuracy
assessment. Remote Sensing of Environment. Vol. 80, pp. 185-
201.
Green, K., Kempka, D. and Lackey, L., 1994. Using remote
sensing to detect and monitor land-cover and land-use change.
Photogrammetric Engineering and Remote Sensing, 60, pp.
331-337.
Ivits, E., Lamb A., Langar, F., Hemphill, S., Koch, B., 2008.
Orthogonal transformation of segmented SPOT 5 images:
Seasonal and geographical dependence of the tasseled cap
parameters. Photogrammetric Engineering & Remote Sensing,
74, 11, pp. 1351-1364
Lambin, E.F., and Strahler, A. H., 1994. Change vector analysis
in multi- temporal space: A tool to detect and categorize land-
cover change processes using high temporal-resolution satellite
data. Remote Sensing of Environment, 48:231—244.
Lillesand, T. M., and Kiefer, R. W., 1987. Remote Sensing and
Image Interpretation (New York: John Wiley & Sons).
Lu, D., Mausel, P., Brondizio, E., and Moran, E., 2004. Change
detection techniques. International Journal of Remote Sensing,
25, pp. 2365—2407.
Malila, W.A., 1980. Change vector analysis: an approach for
detecting forest changes with Landsat. Proceedings of the Sixth
Annual Symposium Machine Processing of Remotely Sensed
Data, Purdue University, West Lafayette, IN, pp. 326-335.
