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Poster Session
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Detection of forest change using multi-spectral
scanner data
- Normalized D-value Vegetation Index Method
Like ratio vegetation index, normalized D-value vegetation index reflects vegetation. In the area of sparse vegetation and great interference of soil background , the application of normalized D value vegetation index is superior to ratio vegetation index.
calculation formlua:
DNDij = ND(2)ij - ND(1)ij + C
NDij(k) = ( MSS7(k)ij - MSS5(k)ij ) / ( MSS7(k)ij + MSS5(k)ij ) ) * CK
where:
DND stands for D-value image; normalized vegetation index; MSS (7) the 7th waveband; MSS 5 the 5th waveband; k temporal; i line, j row and constant. The result of this method is shown as Table 2, The average acuracy is 75.6% total accuracy, 73.7% and comprehensive accuracy, 74.7%.
- Method of Multi-temporal KL Analysis
Two-temporal wavebands of MSS are taken as eight-channel data. The extended data, by KL analysis, separate the vegetation information changes taken as a type of noise from the high order KL producing vegetation dynamic information, in the process of multi temporal structure
protation Landsat image is transformed by means of KL, The first KL is luminace, the second green, greater part of the third and fourth, noise.
Two conditions are needed for carrying out dynamic monitoring by means of multi-temporal KL, e.i., two-temporal images possess dimensionality of two-dimension, namely, luminance and green, Land coverage and changing extent of vegetation exceed a certain limit. With the two conditions, and through exact registration, these multi-temporal multi-dimension data in numeral space rotation produce spectral reflection changes caused by different dynamic changes which are separated each as one-dimension component. Of the new KLs, the four KLs, stable luminance and stable green, changing luminance and changing green are of significance.
Table 2 shows characteristic root and characteristic vector of the KL transformation of MSS two-temporal experimental window images. Each of the first and second KL of two-temporal includes more than 98% of the information of the four original waveband images Consequently, the basic dimensionality of the two-temproal's original data is two-dimensional; each of the first KL of two-temporal is positive value, being luminance of image, accounting for about 90% of total information; while the second KL in waveband of visible-light is negative value, in infrared waveband, positive value. This KL reflects the characteristics of vegetation, called "green".
Dynamic changes can be analyzed from the transformation of multi-temporal KL. From Table 3 it can be seen that the characteristic vectors of the first principal component are positive value, reflecting the stable luminance of multi-temporal image, and including 71.8% of all variable information. Multi-temporal first principal component image reveals that where the luminance is high on the image of different wavebands of two-temporal, the luminance on the first principal component image is also high, such as waste land and farmland, etc. In the first four passages of the second principal component, the characteristic vectors of the first temporal at the four wavebands are all negative values. While in the latter four passages, the characteristic vectors of the second temporal in the four wavebands are all positive values. This principal component reflects the changes of two-temporal luminance. The second principal component image shows that the luminance changes of bench and arable land near gullies and valleys, along rivers and streams, are greater. It can be seen from image of the 16 May , 1976 that because it was spring, soil humidity of these regions was greater; and, hence the luminance in different wavebands was low. However, on the image of the 7th October, 1985, because it was autumn, the bare soil was dry, and the luminance of different wavebands was higher, The general trend of characteristic vectors of the third principal component is negative value at the visible wavebands of different temporals, but positive value at infrared waveband. Therefore, this waveband is stable green. In the third principal component image, where there is vegetation cover, the pixel luminance is higher. The characteristic vectors of the fourth principal component are positive value at the first temporal visible-light waveband and the second temporal infrared waveband; first temporal infrared waveband and the second temporal visible-light waveband are negative value. This principal component gives prominence to changes of the two temporal reflection spectra, caused by vegetation. In this principal component image, the luminance of newly increased vegetation region is high, that of damaged vegetation region, low. Compared with the first four KLs, the KLs of higher orders contain very little information and it is difficult to determine its significance. For dynamic monitoring of forest the fourth principal component is the information we want to extract, which reflects the situation of vegetation changes. The monitoring accuracy of dynamic image separated by 1.50 times of standard difference, using standard difference threshold, is the highest. The average accuracy amount s to 78.4%, total accuracy, 80.64%, and comprehensive accuracy, 79.52%.
Table 2 Characteristics root and characteristic vector of MSS
experimental window image of 1976, 1985
| |
Statistic |
Principle Component |
| 1 |
2 |
3 |
4 |
| 1976 |
Characteristic root |
109.635 |
9.729 |
1.369 |
0.958 |
| Contribution Rate |
90.1% |
8.0% |
1.1% |
0.8% |
| Accumulated contribution rate |
90.1% |
98.1% |
99.2% |
100% |
| 1985 |
Characteristic root |
148.783 |
13.926 |
1.836 |
1.438 |
| Contribution Rate |
89.6% |
8.4% |
1.1% |
0.9% |
| Accumulated contribution rate |
89.6% |
98.0% |
99.1% |
100% |
Table 3
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