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Poster Session
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Detection of forest change using multi-spectral
scanner data
- D-value Method of Ratio Vegetation Index
Since the ratio of IR/RED is closely related to plant biomass, this vegetation index of comparative two-temporal can effectively monitor the forest vegetation changes. At th3 same time, the ratio can also eliminate the influence of the atmospheric condiction, soil moisture and sun angle on the image, and reduce the difference of changes not caused by the type of land, and , consequently, the changes of land type become conspicuous.
Calculation formula:
DRij = (MSS7(2)ij / MSS5(2)ij ) C2 - ( MSS7(1)ij / MSS5(1)ij ) C1 + C
DR(ij) ratio D-value image; MSS(1) first temporal; MSS(2) second temporal;
C, C1, C2 constant; i line; j row.
On the ratio image, pixels of high luminance mean the sharp increased area of ratio vegetation index, while low luminance the sharp decreased area of ratio vegetation index; a greater part of pixel assumes intermediate grey, signifying an area of slight change of vegetation index. In view of the distribution of histogram pixels of the violently changed vegetation index distribute at the two tails of the histogram At the left tail is the distribution of pixels of sharply decreased vegetation index, while at the right tail is the distribution of pixels of sharply increased vegetation index. Vegetation index changes slightly in a greater part of area and distributes in the intermediate position of the histogram. The luminance distribution of the entire vegetation index D-value image is continous. The two-temporal vegetation index changes are as follows:
- Changes are caused by the difference of seasons. However, if only it is in the growing season, no great changes will take place with the changes of seasons.
- Changes are caused by those of vegetation viability. Nevertheless, owing to the long growing cycle and slow senility of forests, and in the condition of short plastochron, changes of vegetation index will be slight.
- Changes of vegetation index are caused by the succession and reform of forest vegetation.
Changes caused by this factor, in fact, are different from those of vegetation index caused by vegetation type. However, the former changes, in general, are not great either.
- Changes in the growth and decline of forest vegetation are, mainly, caused by felling, fire and afforestation. The sharp changes of vegetation index caused by this factor are even greater that those caused by other factors.
Based on the fact that the growth and decline of forest vegetation exerts a remarkable influence on the changes of vegetation index, the changes of forests can be monitored according to the changes of vegetation index. Dynamic monitoring of forest area, in general, can be divided into three kinds, namely, newly-increased forest land, untouched forest land and damaged forest land. For this reason, vegetation index D-value image must be divided according to a certain threshold to detect the position and size of the changed area. In order to determine the division accuracy of different threshold, 225 sample points are set up at the entire experimental window. According to the standard of dynamic results interpreted by two-temporal aerial pictures, accuracy of different thresholds is checked and taken. Consequently, optimum threshold is determined. During the interpretation of aerial photos, in consideration of spatial resolution of image MSS and classification method of forest investigation of our country, changes of forest land are determined as follows:
newly-increased forest land:
forestless land---> forest land, shrub forest land
thin stocked land---> forest land, shrub foreest land
damaged forest land:
forest land---> forestless land, thin stocked land;
shrub forest land---> forestless land
In order to estimate the accuracy of dynamic monitoring, during calculating, monitoring accuracy, average accuracy and total
accuracy should first be calculated respectively, and the average value of both is taken as the criterion to compare different thresholds (see Table1) .
Table 1 shows that monitoring accuracy detected with 1.25 thime s of standard difference is the highest; average accuracy amounts to 78.5% and comprehensive accuracy, 76.85%.
Table 1 Image threshold detection table of ratio vegetation D-value
standard difference
time K |
average value x=89. 177 standard difference STD=15.288 |
| accuracy of correct classification unit % |
| average accuracy |
total accuracy |
comprehensive accuracy |
| 0.75 |
68.53 |
69.78 |
69.20 |
| 1.00 |
70.32 |
72.40 |
71.36 |
| 1.25 |
78.50 |
75.20 |
76.85 |
| 1.50 |
73.35 |
74.35 |
73.79 |
Calculation methods of different accuracy in Table 1 are as follows (the same with others)
average accuracy = (( monitoring accuracy of correct changes + accuracy of correct non - changes) / 2) 100%
total accuracy = (correct total / total number of samples ) x 100%
comprehensive accuracy = (( average accuracy + total accuracy ) / 2 ) X 100%
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