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Forestry
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Forest change detection A study of the flooded area in king amphoe phipunProvince of Nakhon SI Thammarat, Thailand
The Change Detection
- Preprocessing
- Geometric correction
A Landsat MSS scene registered on the 12th of April 1984 and a Landsat TM scene registered 4 years later, on the 30th of March 19088, were compared to detect the change of the vegetation during that period. The MSS image was geocorrected using a third degree polynomial transformation and resampled from 80m to 50m-pixel size. The TM image was geocorrected applying the same kind of transformation and resample from 30m to 50m-pixel size. The images differ from each other with in average less than 0.5 pixels in both X and Y.
An area of 24*24 km, covering the region worst struck by the flood, the river plain and the water catchments area of the two river channels where most of the mud and debris came down, was chosen as study area.
- Sensor characteristics
Because of differences in the spectral characteristics of the sensors of the multispectral scanner and the Thematic Mapper. Only the bands corresponding in both satellites could be used in the principal components analysis. MSS band 1 and 2 (visible green and visible red) correspond well to TM band 2 and 3 respectively, but when it comes to the invisible reflected infrared band, which is very important for determining biomass content, the sensors differ some. However, the band 4 covers most of MSS band 4 and the two were therefore chosen as the third pair of bands to be compared in the principal component analysis.
- ls excluded from the computations
When it comes to remote sensing data, and especially of this latitude it is a well known fact that we often have problems with cloud cover. Although the MSS image in principle was cloud free, we still had the consider this problem since the TM image contained some scatters clouds the mountains in the northern part of the study area.
Apart from the fact that clouds actually conceal information in the images, they also have a strong effect on the principal components analysis. Clouds will be interpreted as areas of very strong change and this results in that the effect of areas actual change will become very faint. Pixels where clouds or cloud shadows could be found were therefore set to zero in both images and excluded from the computations.
Since the investigation concentrated on the
change in the mountainous areas. It was also possible to improve the
accuracy by excluding pixels on the flat land beneath the mountains.
Points below the altitude of 100 m were therefore considered to be of
less interest although some may have had occurred.
- Principal Component Analysis (PCA)
- Some theory
The basic principal of the detection is that areas that have not changed in the period in question will have approximately the same not changed in the period I both images and therefore be highly correlated, whereas areas of significant change consequently will have low correlations.
The property of a principal component transformation is that it reduces redundant information in the input data (in this case 6 channels; 3 from MSS and 3 from TM) and creates a new set of coordinate axis (here 6) where the data in uncorrelated. Areas of significant change will then occur a high or owl values in the higher principal components.
- The analysis
From Table 1 it can be seen that the first principal component (PC-1) contains most of the variance in the data, PC-2 contains the next major part and so on. PC-5 and PC-6 have very low variances and consist mainly of noise.
Table.1 Information contens of the principal components
|
PC-1 |
PC-2 |
PC-3 |
PC-4 |
PC-5 |
PC-6 |
| Variance |
3.436 |
1.567 |
0.57 |
0.349 |
0.072 |
0.018 |
| Info. Content (%) |
56.3 |
26.1 |
9.3 |
5.8 |
1.2 |
0.3 |
| Cumulative (%) |
57.3 |
83.4 |
92.7 |
98.5 |
99.7 |
100.0 |
Areas of significant change could be found in PC-3 as especially high and low values. The high values represent change from vegetation 1984 to non-vegetation 988 and the low values indicate change n the other direction. The class non-vegetation includes bare soil as well as very sparse vegetation where the ground still can be seen from the air. When the thresholds for the areas of change had been found, PC-3 was classified. Some noise was reduced by using a threshold in PC-1 as well.
- PCA Results
The analysis show that a total area of 1510 hectares had been changed into non-vegetation during these 4 yeas and that only 210 hectares had recovered and changed in the other direction. This totals in a net decrease I the vegetation cover with 1300 hectares. The figures are of course approximate but they give an indication of the magnitude of the change.
Although the image quality of Fig 2 is bad, the detected change from vegetation to non-vegetation can be seen as white spots, especially on the mountains sides closest to the flat land in the valleys.
Fig.2 King Amphoe Phipun. hill shading from DTM
(24 km * 24km). White spots: vegetation 1984=
> non-vegetation 1988
- Land cover classification
In order to check the correspondence with the PCA, as well s to find out the present land use (i.e March 1988) in the area, both images were classified using Maximum likelihood method. However, since no ground truth information of the area was available and an on-site investigation not yet had been performed, only three classes were used, i.e. bare soil, parse forest and dense forest. Baresoil had the same meaning as in the PCA: bare soil and very sparse vegetation where the soil can be seen from the air. Spare forest includes young rubber and orchards while the dense forest class includes natural forest and most probably also dense rubber plantations.
As for the PCA, pixels below 100m as well s cloud pixels in either of the images were excluded from the classifications.
- Results
The results of the two classifications can be found in table 2 it can be seen that approximately 3500 hectares of dense forest in the test area had been converted into some other kind of land use during these 4 yes, a decrease with 14%. The areas of sparse forest and bare soil increased at the same time with 18% and 20% respectively.
Table 2: Maximum likelihood classification
| Land type |
MSS 1984
hectares |
TM 1988
hectares |
Change
hectares |
Ratio
1988/1984 |
| Bare soil |
560 |
1850 |
1290 |
3.30 |
| Sparse forest |
12480 |
14710 |
2230 |
1.18 |
| Dense forest |
26010 |
22490 |
-3520 |
0.86 |
| Total |
39050 |
39050 |
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The 1290 hectare figure for bare soil can be
confirmed by comparing it with the PCA, which showed a net of decrease
of 1300 hectares in the vegetation. The correspondence is almost
surprising while the figure itself if depressing.
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