Full text: Remote sensing for resources development and environmental management (Volume 1)

differences between the Landsat MSS and SIR-B radar obviously 
complicated their co-registration. 
Once the Landsat and SIR-B were successfully co-registered, 
maximum likelihood classification of the Landsat and SIR-B images 
was undertaken. Landsat and SIR-B images were also separately 
classified, as were the first three principal components of a 
principal components transformation of the combined Landsat and 
SIR-B images. 
2. Methods 
All processing of the SIR-B and Landsat data was performed on a 
Dipix Aries II image analysis system at the Centre for Remote 
Sensing, University of New South Wales. 
A static average filter was chosen to reduce speckle. It 
consists of a grid cell moving over the raw radar image, the size ol 
which is user specified (in our case a 2 x 2 cell). The algorithm 
calculates the average value for all pixels within the grid cell, and 
-this value then becomes the new grid cell value, replacing all other 
pixel values. This filter effectively reduced the 12.5 m pixel size tc 
25 m, as well as lessening the speckle effect. 
Co-registration of the two images was multistaged. The SIR-B 
image was rotated clockwise through 300 degrees to align it with 
the Landsat image. Next, the SIR-b image was registered to the 
Landsat image by a first order polynomial using 11 well distributed 
tie-points. Cubic convolution interpolation resampled the SIR-B 
image to the same resolution as the Landsat. Corresponding ground 
control points on the Landsat band 7 image and the basemap (a 
composite 1 -.100,000 topographic mapsheet with an Australian Map 
Grid (AMG) at 1 km intervals) were located and used to calculate a 
second order polynomial with acceptable residual errors. Geometric 
correction of both the Landsat and SIR-B images was performed with 
this single polynomial function. Cubic convolution interpolation was 
again used to resample the Landsat and Sir-B pixel size to 50 m 
Three separate maximum likelihood classification strategies 
were attempted on the full five feature combined Landsat and SlR-B 
data set; on the first three principal components of the full five 
feature data set; on the four feature Landsat MSS data; and on the 
SIR-B data alone. A representative subscene encompassing all the 
major cover types (viz. Box forest, River Red Gum forests of site 
qualities 1,2 and 3, swamp, water and agriculture) was selected to 
/educe the classification time. 
The classified maps produced were quantitatively assessed fot 
accuracy using a modified stratified random sample technique 
proposed by Kalensky and Sherk (1975). This strategy provides an 
estimate of mapping accuracy or the image (i.e. it accounts for 
positional accuracy) as well as the more frequently quoted 
classification accuracy. The sampling intensity was estimated using 
the procedure developed by Van Genderen et al. (1979), where taking 
30 randomly located sample units per strata gives a classification 
accuracy estimate within 95 per cent confidence limits. 
3. Results and Discussion 
The static average filter used to reduce radar speckle did not 
degrade edges and boundaries to the same extent as a mean filter, 
which was also run on the data. The other advantage of the static 
average filter was that spatial resolution was reduced in one 
operation without having to interpolate using cubic convolution or 
nearest-neighbour and attract the respective disadvantages of these 
techniques Tviz. an overly smoothed image or a reduction in spatial 
accuracy). Figure 2 shows the radar image after speckle reduction 
and qeometric correction. 
Co-registration of SIR-B radar and Landsat MSS proved 
acceptable, with residual errors of less than one third of a pixel. 
Visual comparison of the Landsat and SIR-B images, and the 
base-map indicated that the registration had been very successful!. 
Figure 2 - Radar image after speckle reduction 
The Landsat MSS and SIR-B images displayed many prominent 
features which could be readily ground truthea to known surface 
features. The Landsat colour composite showed differences in 
vegetation vigour within the forest, with the Red Gum site quality 1 
ana site quality 2 stands (which had recently been flooded) appearinc 
much "redder. As the vegetation became drier (i.e. vegetation on 
higher ground) the tone became less dense and the colour lighter. 
Water and swamp areas on both the Landsat and radar appeared dark. 
Inspection of the SIR-B image, and investigation of bispectrai 
plots of cover class pixel dumps for the SIR-B and Landsat images, 
the following qualitative assessment of the range of radar 
brightness value responses for the cover classes was developed 
(Table 3). 
Table 3 - Range of radar brightness values for the cover classes 
Bright ¡«Intermediate >Dark 
or cover 
Red Gum SQ1 
Red Gum SQ2 
Red Gum SQ3 
Water, swamp and agriculture appeared dark as they acted as 
specular reflectors away from the satellite's receiving antennae. The 
forest complex is essentially uneven-aged, and so the density of the 
different forest types generally reflect the site quality (Forestry 
Commision of N.S.W.). in general, the denser the forest type (i.e. the 
higher the basal area or stocking), the brighter the radar response. 
This observation agrees with hoekman (1985), who found that 
backscatter from XT>and SLAR increased for increasing age (or 
density) for spruce plantations in Holland, (during the needle 
forming period tor the tree). Wu (1984) also found that for L-Band 
SIR-A data, radar return strength is highly correlated with tree 
height or age (and hence density) for three types of pine forest in 
South Carolina, U.S.. 
Wu (1984) also found that X-band SAR had higher backscatter 
for cypress forests over standing water. This phenomena may be 
occuring over the Riverina forests, which were subject to an 80% 
flood at the time of the SIR-B overflight. This aspect is to be 
discussed in a forthcoming paper by the authors. 
Supervised classification yielded the overall classification 
results shown in Table 4. Note that as the SIR-B data classification 
result was so poor, a detailed estimate of mapping accuracy was nol 
attempted for that data source alone. 
Table 4 - Overall classification results for the supervised 
Data type Overall classification accuracy 
Landsat and SIR-B combined 65.1% 
Landsat only 602% 
First 3 principal components 56.4% 
Landsat combined with SfR-B gave the best overall result. 
SIR-B obviously provided additional information which increased 
classification accuracy, but the amount of contributed information 
is poor in comparison to that obtained from the MSS. The Landsat and 
SIR-B in combination also gave the best individual class mapping 
accuracies, and the confusion table (after Kalensky and Sherk, 1975) 
is shown in Table 5. 
~Nb other workers have attempted to map site quality classes 
for uneven-aged forests using remotely sensed data, so these result! 
are not comparable with previous work. However, Benning et al. 
(1981) had limited success at classifying exotic forest types into 
age classes using Landsat data alone in New Zealand., with 
classification accuracies ranging from 16% to 58%; a poor result 
compared with that stated here. Inkster et al. (1980) used a four 
channel SAR over forested sites in Canada and estimated the 
resolution required for accurate forest mapping was 6 m. Guidon et 
al. (1980) used Landsat MSS data, an 11 channel airborne MSS and a 
four channel airborne scanner over rugged forest terrain, and showed 
higher classification accuracies were possible with the MSS imagery 
than with SAR imagery, which agrees with our results. The airborne 
MSS gave much better results than the Landsat MSS, due to the 
superior resolution and spectral range of this scanner. 
Supervised classification of the first three principal 
components proved less accurate overall than the Landsat MSS data 
alone. Inspection of the eigenvector-eigenvalue matrix produced by 
the principal component analysis showed that 84.2% of the total 
Variance in the first three principal components was due to the radar 
The radar, which dominated the first three components, generally 
'shows only minor discrimination of the forest.

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