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Forestry
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Remote Sensing for forestry applications In the tropical rain forest of Peninsular Malaysia
Materials and Equipments
SPOT HRV (6 July 1987, path 269, row 343, @CNES 1987) and Landsat. TM (29 February 1988,
path 127, row 58) images were used in this study. Because of rough ground resolution, TM 6 was not utilized. About 20% of both images were covered with clouds and the HRV image did not cover about 220% of the eastern study area. The sun elevation and azimuth angles of the TM and HRV images were 51o 35 and 6o , 46', and 60o 08' and 46o 54 respectively. Differences in the sun's location was a factor leading to errors in change
detection.
Topographic maps (scale 1:63,500) with forest compartment boundaries were used for selecting ground control points, making a digital elevation model and digitizing compartment boundaries.
A Fujitsu M340 s computer was used for basic data processing, and as an International Imaging System's (1Z S ) system 600 image processing system on a Toshiba AS3160HM computer (OEM of Sun 3-160) was used for advanced data processing. A drum scanner produced by the Sekkej inc. was used for digitizing maps
Method
- Preprocessing
Geometric correction was executed on Landsat TM data with pixel size of 2225 m using 26-ground control points on the longitude and latitude coordinate. The HRV data were registered to the TM data using 19 control points. Second-order polynomials with nearest neighbor resembling was used for both corrections.
Elevations were digitized with pixel size of 200 m and registered to the images using bi-linear interpolation. Then 1000 m contours were delineated. Forests compartment boundaries were digitized by the drum scanner, then they were thinned and overlaid on the images.
- Image Enhancement
Desirable channel combinations were checked for making forest-interpretative color composites of the TM data by transformed Divergence (TFD) (Swain et. al. in press). Mean values of the TFD were used for selecting the best combination of three channels (Table 3). Categories used in the analysis were wamp forest, clear-out forest, four different stages of rubber, Acacia mangium, three different stages of oil palm, pine, logged-over forest, secondary forest, secondary to primary forest and primary forest. Then histogram normalization referring to vegetation training areas and Wall is enhancement were consecutively applied to several channel combinations in accordance with the former results. Tested combinations were TM 4,5,1, TM 4,5,2,Th 4,5,3 and TM 4,5,7 (RGB) (Figure 4).
- Change Detection
The NVIs of TM and HRV, along with a normalized difference ratio (TM5-Tm4)/(TM5+TM4) (NDR54) image were produced after subtracting the minimum value of each channel for adjusting the origin of two dimensional space (Crippen 1988). The NDR54, after histogram normalization was processed, was used supplementary to make a color composite.
Two image processing were executed to enhance forest changes, especially cutovers, using the TM and HRV data. The first was, PCA using TH 2,3,4 and HRV 1,2,3 (pair-image PCA). The second was PCA using TM and HRV's NVI (NVI-PCA). Then several color composites were examined and selected combinations were the 4th, 3rd and 2nd components (RGB) for the pair-image-PCA, and the 2nd component, Ist component and NDR54 (RGB) for the NVI-PCA. Wall is enhanced TM 3,4 and HRV 2 (ORG, in RGB) was used as a reference image. Cutover means a logged-over forest between observed dates of the two images in this paper.
- Superimpossion
Forest compartment boundaries, compartment numbers and 1000 m elevation contours were superimposed with the color composites and their effectiveness was examined.
Discussion
- Image enhancement
Several TM channel combination studies have been reported (Awaya et al.1985 etc.) and the common results have been that combinations including TM 4 and 5 were highly ranked for effective channel selection. the TFD analysis showed almost the same results, but TM 7 seemed to be more effective than other studies' results (Table 3). Miwa also mentioned TM 4, 5and one other channel in coloring red, green and blue as an adequate color combination. The color composite usually shows vegetation differences very well.
Histogram normalization was effective in enhancing less varied channels in forests and various color tones were recognized. Then Wallis enhancement contrasted borders of different forests (Figure 3)in all channels and improved color balance of the composites very well. As Wallis enhancement is a space variant function which enhances pixel by means and variances of small local windows, clouds badly effect its application for original TM data. Thus pre-enhancement was necessary to reduce the effects.
The effectiveness of the third channel was also checked (Figure 4). The Ulu Gomak protected forest was strongly enhanced by TM 1 (dark at bottom center) and it contrasted very well with the forests on step slopes in the Genting Highlands (medium brightness at upper right) or secondary forests (slightly dark at middle left). On the other hand, the brightness differences were reduced in other channels as wave lengths were longer.
The color composites were compared with each other (Table 6) using the compartment superimposed products, which made it possible to distinguish forest types in each compartment (Figure 5, Table 2). The most colorful and interpretative composite was TM 4,5,1. Basically colors were identified in the forests in the TM 4,5,1 image, but they became less visible as the wavelength become longer in the blue colored channel. A ground truth study must be executed to make sure of the relationships between the colors and forest types. The compartments made it possible to identify forest types at exact locations which is useful information for forest management.
- Change Detection
Change detection using two images under very different solar angle is extremely difficult task because of various shadows effects in mountainous area. Detailed spectral adjustment between two corresponding channels such as using linear regression (Fogelman 1988, Ohnuki et al. 1988) was nearly impossible without topographic correction.
PCA is a very popular transformation for reducing data dimensions and enhancing images, but it is a data dependent transformation and produces various results case by case. Thus the training area which is desired to be enhanced should be appropriately selected for PCA. However PCA has the following advantages. 1) As the first component has the greatest variance of data space, the lower components might be less effected by topography. 2) Although a linear regression cannot express the longer axis of data distribution in a low correlated two dimensional space, the first component is robust enough to express it and the longer axis usually shows the most common relationship between the samples. 3) PCA using a correlation matrix reduces the difference of sensor sensitivity.
Vogelmann (1988) pointed out that A drawback of PCA is the difficulty of relating the results to reflections change for individual bands for the areas undergoing change." As he indicates, slight changes might be undetectable but reflection changes distinctly after logging roads, logging yards and badly damaged forests are detectable by PCA.
The 1st component of the pair-image PCA showed the total brightness of the images, but it was effected by baze and topography, and this was why the component wasn't used in the color composite. However the topography became invisible with the lower component. The 2nd component enhanced general differences between the two images and the 3rd component displayed vegetation. Bare soil and cutovers were also enhanced in the 3rd and 4th components respectively (Table 4, Figure 6).
Rationing is a well known technique for removing shadows caused by topography (Crippen 1988), and NVI was performed instead of direct rationing to enhance non-vegetated areas, and topographic effects were invisible in the two NVI of TM and HRV. Cutovers appeared dark in the 1st component and bright in the 2nd component of NVI-PCA, but clouds is the TM data were also enhanced in the same way. It seems that change detection would be impossible, if clouds and their shadows should not be removed at data processing or be superimposed on the final results. Rare soil such as logging roads was contrasted with cutovers in the 2nd component with dark brightness (Table 5, Figure 7).
The color composites were compared (Table 7). NVI-PCA strongly enhanced cutovers and bare land such as logging roads, and topographic effects were almost completely removed. However cutovers and bare land could not be distinguished from clouds and their shadows and it was almost the same in the pair-image PCA composite. Rapid vegetation growth was distinguished, but topographic effects were visible in the pair-image PCA. Colors of cutovers were varied in the ORG composite, but clouds and their shadows were distinguishable.
As compartment superimposed products showed exact harvesting locations and neighborhood conditions, they could be effectively used for change detection (Figure 8). For example, as legal harvesting was executed by private companies with licenses (MPI 1988), illegal harvesting would be detectable easily by checking each cutover's location and extent.
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