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Poster Sessions
  • Poster Session 1
  • Poster Session 2
  • Poster Session 3



  • ACRS 1997


    Poster Session 1
    A Study Of Land Use/Land Cover Changes From 1988 To 1994 In Nilai and Surrounding Areas

    Methods
    The changes detection techniques to be used are Normalized Difference Vegetation Index ( NDVI) , Principal Component Analysis ( PCA), Image Differencing and Image Rationing . the two images used in this project were geometrically corrected by using image to image registration. As forester ( 1990) said , " image to image registration is two to three times more accurate than image to map registration ' and also the first order is adequate ( Forster, 19950.

    Normalised Difference Vegetation Index ( NDVI)
    For the change detection analysis, the bands for both years were combined as below:

    Table 1. band combination for year 1998 and 1994
    Year Old band New band
    1994 1 1
    1998 2 2
    1994 3 3
    1988 4 4
    1988 3 5
    1994 2 6
    1994 4 7

    The NDVI was performed by ( Band 4- band 3)/ ( Band 4+ Band 3) and the images is plate 3. from the image, areas where vegetation has been cleared between 1988 and 1994 is white, ' developed ' areas in 1988 and 1994 is black and the areas where ' development ' occurred in 1994 is grey in colour .


    Plate 3: Change Detection using NDVI

    Principal component Analysis ( PCA)
    Principal component was performed for each data sets- Landsat TM 1988 and 1994. the results of the PCA are given in Table 2 and Table 3 below. It shows that from the first four PCA, there were some changes occurred in Nilai an surrounding areas.

    Table2: Eigenmatrices and eigenvalues for year 1988
    TM components
    Bands 1 2 3 4 5 6 7
    1 0.42 -0.01 -0.26 0.33 0.78 0.17 -0.02
    2 0.43 0.03 -0.23 0.29 -0.56 0.61 0.06
    3 0.43 -0.09 -0.23 0.30 0.27 -0.75 -0.17
    4 0.13 0.94 0.26 0.11 0.01 -0.10 0.12
    5 0.42 0.09 0.06 -0.62 0.12 0.10 -0.64
    6 0.29 -0.31 0.87 0.25 0.01 0.03 -0.01
    7 0.43 -0.10 -0.02 0.50 0.02 -0.11 0.73
    Eigen value 26849 5391 3323 1457 488 267 153

    Table 3: Eigenmatrices and eigenvalues for year 1994.
    TM component
    Band 1 2 3 4 5 6 7
    1 0.41 -0.09 -0.13 0.47 -0.76 0.08 -0.01
    2 0.42 -0.05 0.07 0.31 0.49 0.69 -0.003
    3 0.42 -0.14 0.03 0.37 0.40 -0.71 -0.04
    4 0.10 0.96 -0.14 0.13 0.03 -0.07 0.15
    5 0.41 0.18 0.36 -0.42 -0.11 -0.01 -0.69
    6 0.34 -0.10 -0.83 -0.43 0.07 0.004 -0.01
    7 0.42 -0.05 0.38 -0.41 -0.10 -0.04 0.70
    Eigen value 26696 5507 2024 1728 1728 429 232

    Image Differencing
    Image differencing was performed by subtracting B41994 for B41988 and the image is as Plate 4. Most of the changes were due to the vegetation changes either by cultivation or land clearing for development purposes by the state government. Light areas indicate that land cover changed from vegetated areas in 1988 to ' developed ' areas in 1994; from less dense ' kampungs' in 1988 changed to more densed ' kampungs' in 1994 and there were more green vegetation in 1988 than in 1994. the black areas were the areas the vegetation increased I 1994. these were due to the new settlements with young plantations in 1988 became matured in 1994, or either the ' kampungs' areas converted to the plantation areas. The gray areas were the areas the experienced no change in 1988 and 1994.


    Plate 4: Change Detection using image Differencing

    Image Ratioing
    For change detection analysis, band 4 ( NIR0 was used to see the changes in vegetation cover in the study area ( Band4 1994 / Band 41994) and the image is as Plate 5. from the output images, it is clearly shown that the white region were the regions that have more vegetation in 1988 than in 1994. the grey regions were the unchanged regions between 1988 and 1994. whereas the black regions were the regions where it was more vegetated in 1994 than in 1988. this was due to the fact that in 1988, the new settlement areas with young plantations became matured plantations in 1994 and also some of the 'kampungs' were converted to the plantations area.


    Plate 5: Change Detection using image Ratioing

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