Detection of forest change using multi-spectral
scanner data
Xu Dingcheng, You Xianxiang, Han Xichun
Beijing Forestry University, Beijing, China
Abstract
In the experiment, MSS satellite data tapes of two periods (May, 1976-October, 1985) were using and the following methods which were studied include image D-value, D-value of ratio vegetation index, Normalized D-value vegetation index, Multi-temporal KL analysis and Monitoring of classified comparison. These methods have been used in the forest dynamic monitoring.
Research on region information acquisition and methods
The experimental region is located in Pingquan County, Hebei Province (at 41°2'19" - 41°14'10" N and 118°31'43" - 118°47'36" E). The area of experiment is about 600 square kilometers, Two MSS data tapes of two periods were used in the experiment (see the following table.)
|
satellite number | 2 | 5 |
| date | Many 16,1976 | October 7, 1985 |
| index number | 13/31 | 13/31 |
| sun angle | 56° | 48° |
| position angle the sun | 123° | 135° |
Besides, the following aerial photos were also used in the experiment:
Black-white aerial photos 1:50,000, taken in 1979; infrared color aerial photos (1:,30,000, 1:70,000 and 1:130, 000)' and a number of forest distribution maps in different periods and topographic maps, etc.
In the experiment, binary cubic multinomial and bilinear interpolation was adopted to take samples and the temporal image MSS of 1985 was corrected. During the correction 19 control points were evenly used. After correction, the image of 1985 was taken as the standard, while the image of 1976 was registered with that of 1985. The mean square root errors in X and Y after recombination are 0.331 pixel, and 0.274 pixel, respectively.
Dynamic monitoring method of forest area
Dynamic monitoring of forest area was conducted by mans of the difference between the two-temporal images. The two-temporal changes can be divided into two kinds The first kind includes atmospheric condition, soil moisture, the difference in satellite detection process, etc.
which influence most or all pixels. The influence can be eliminated or decreased through operations or rotary data space. the second involves only part of pixels, such as forest felling, afforestation regeneration seasonal difference etc.
To collect the information of forest dynamic changes, the following methods were used in the experiment:
- Method of Image D-value
The growth and decline of forest will induce the changes of images of red-light waveband as well as near infrared waveband. The method of image D-value is used to substract lumin-ance value of the first temporal image from the matched original image of the second temporal. Theoretially, positive and negative values indicate the changed pixels and zero indicates the pixels without changes.
Since the luminance value of image is between 0 and 255, a constant is usually added in the method of D-value to eliminate the negative value.
The formula is as follows:
DX ( ijk ) = X( 2 )( ijk ) - X ( 1 )( ijk ) + C
where
DX indicates changed image
X (1), X (2) indicate the first and second temporal images
C indicates constant; i indicates line;j
indicates row; k indicates waveband.
The histogram of the D-value image MSS7 and MSS5 produced by the method of image D-value isdistributed like a bell in shape (see Figure 1).
Fig. 1. Diagrammatic sketvh of D-value image threshold detection
Although the two wavebands are highly sensitive to vegetation changes, the combination of the changes of pixel luminance caused by aspection difference, atmosphere, location of satellites and difference of soil moisture, and the changes of vegetation coverage makes the two kinds of changes unable to be separated. As a result, it is difficult to collect all of information, While the influences of different factors on different wavebands are not the same, the colour combination of the D-value of 3 wavebands synthesizes the dynamic information of different wavebands. Therefore, the information of vegetation changes can be conspicuous.
If D-value images MSS7, MSS5 and MSS4 are respectively matched with the colour synthetic images of red, green and blue, the increased area of vegetation will be red; the damaged area of vegetation is dark drown; the greater part of cyan represents type of land without any change. Simple D-value synthetic image produces excellent visual interpretive effects.
