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Land Use
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Land Cover Change Detection Radio metrically-Corrected
Multi-Sensor Data
The third component (gs3) exhibits almost similar component loading between the two data sets but in different. The table shows that SPOT HRV data contribute negative loading whereas Landsat TM data contribute positive loading to the component image. As expected, the component image (gs03) shows area of change from vegetation to non-vegetation. The area of change is visually distinguishable, in that it appears dark in the component image. Different degrees of brightness can also be seen in the vegetative area,
which represent different type of vegetation. This corresponds to the graph shown in Figure 2, which shows substantial reductions in pixel values in area of change. Examining the area of change in the input data sets (corrected landsat and SPOT data), it appears that most of the changes are from vegetation to non-vegetation.
In the fourth component (gs4), the transformation coefficients in the visible bands of both data sets seem to be in reverse direction as compared to third component (gs3). Again, near-infrared channels of both data sets dominate the component loading are similar to third component. Visual inspection of the component image indicates change in surface feature between two data sets. Changes in cover types from non-vegetation to vegetation cover are shown in dark pixels, whereas non-vegetative cover for both change and unchanged areas appear bright. Similar to the third component (gs03), different variations in grey tone are also observed in vegetative areas. These indicate different type of vegetation cover. This phenomenon can be seen graphically in Figure 2. The fifth component shows the residual information on the GSO transformation. From the image it appears that this component contributes very little information on the study of change.
| No |
Class-Name |
No. |
Class_Name |
| 1 |
Water |
6 |
Stressed Rubber |
| 2 |
Forest |
7 |
Mixed Horticulture |
| 3 |
Mature Oil palm |
8 |
Bare soil/urban area |
| 4 |
Young Oil Palm |
9 |
Vegetation to Bare |
| 5 |
Mature Rubber |
10 |
Bare to Vegetation |
Figure 2: Pixel value of various covers type derived from GSO techniques.
Conclusions
This discussion shows that multi-temporal and multi-sensor data can be made quantitatively comparably by converting the data into a common scale or datum. This is particularly useful in the continuous assessment of land surface feature, in which archived data are to be used. The Gramm-Schmidt Orthogonalisation method appears to be a much more promising technique not only in detecting land cover change but also providing reliable information on the nature and type of change that is taking place over a period of time. Furthermore this technique is independent of the image data, thus permitting the technique to be used with confidence.
Acknowledgements
The authors express their gratitude to the Director General of the Malaysian Agricultural Research and Development Institute (MARDI) for financial support during the study, and to colleagues at the Department of Geography, University of Nottingham, for their support and encouragement. The Malaysian Remote Sensing Centre, kuala Lumpur, Kindly supplied the satellite data used in this work.
Reference
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Collins, J.B., and Woodcock C.E., 1994, Change detection using the Gramm-Schmidt Transformation applied to mortality. Remote Sensing of Environment, 50, 267-279.
- Jackson, R.D, 1983, Spectral Indices in n-space. Remote Senisng of Environment, 13, 409-421.
- Mather, P.M., 1987, Computer Processing of Remotely Sensed Images. (Chichester: John Wiley).
- Mispan, M.R. and Mather, P.M., 1997, Multi-Sensor Radiometric Correction: A Case Study from Malaysia, Paper Presented At the 18th Asian conference on Remote Sensing, 20 -25 October, 1997, Kuala Lumpur,
- Mispan, M.R., 1997, Multi-sensor remote sensing data for change deection: A case study from Peninsular Malaysia, unpublished Ph.D. thesis, University of Nottingham, 221 pp.
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