Full text: XVIIIth Congress (Part B7)

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For the first'test site, our methods included the 
classification and interpretation of the microwave 
and optical remote sensing data, field checks, 
change detection analysis, and comparison of the 
results of different types of data. 
From the additional information acquired in the 
field, a supervised classification was performed on 
the available Landsat MSS (all bands) as well as 
TM data (bands 2, 3, 4 and 5). Training samples 
were first selected from various land cover types: 
wetland rice field; shifting cultivation and 
secondary forest; bush and scrub; lowland forest 
(primary forest); settlement; sea beaches and bare 
soil; sea and lake. After selecting the training 
samples, a classification was run using a 
maximum likelihood algorithm. A quantitative 
evaluation of both classification results was done 
by testing the accuracy using a confusion matrix 
which showed the overall and average accuracies 
by class. 
An attempt was also made to incorporate radar 
data (ERS-1 and JERS-1 data) in multispectral 
classification by combining ERS-1 with Landsat 
TM3, TM4 and TMS. Similar combinations of 
JERS-1, TM3, TM4 and TMS bands were also 
classified. 
Because ERS and JERS radar data are acquired in 
single bands (e.g. wavelength, polarization, 
incidence angle), digital image processing 
techniques are limited. It was therefore decided to 
print the radar images in hard-copies for visual 
interpretation. In this case, other information (e.g. 
contextual or spatial) can be used as key 
interpretation elements to delineate the boundaries 
of different land covers. The visual interpretation 
maps of the ERS-1 and JERS-1 images were 
digitized, polygonized and rasterized for 
comparison to see how many classes could be 
recognized in each of them. Later, both 
Interpretation maps were rasterized to 30 m pixel 
Size and registered to the 30 m spatial resolution 
of TM, MSS and other materials. 
The Landsat MSS classification map of 1973 and 
TM classification map of 1990 were compared to 
calculate the land use changes (e.g. decreases or 
Increases in the areas of different land use classes) 
during 17 years. Another comparison of a 1988 
Landsat MSS classification map, land use/land 
cover map and Landsat TM classification map of 
1990 was made to detect the changes in land uses 
during that period. Emphasis was put on certain 
Classes that were expected to have significant 
changes (e.g. forest, agriculture, and settlement 
Cover types). 
Because urban areas can be detected best on ERS- 
1 radar Images, a change detection analysis was 
performed by overlaying the settlement area from 
317 
the ERS-1 image on the TM classification. The 
same step was repeated by overlaying the 
settlement maps from the 1973 MSS and 1990 TM 
classifications to show urban development during 
17 years. 
Methods used with the data of the second test site 
included: image pre-processing (c.g. radiometric 
and geometric correction and filtering), object 
identification and detection, image classification, 
optical and radar satellite image fusion and 
comparison to detect forest and deforested areas. 
3. RESULTS AND DISCUSSIONS 
Using the MSS data it was not possible to have 
more than eight classes because of the overlap 
between the clusters. It was especially difficult to 
separate different forest types (swamp forest, tidal 
forest, natural forest and rubber plantation) 
because the forest types are not homogeneous in 
terms of the tree species. Each forest type consists 
of many tree species, resulting in mixed spectral 
reflectance characteristics. Thus it was decided to 
combine some of these classes. 
Shifting cultivation and secondary forest were 
placed in one class because the farmers cultivate 
several crops (such as coffee, rubber, cereals, etc.) 
which makes it spectrally confusing with other 
cover types. In addition, the farmers do not clear 
the area completely. It was difficult to distinguish 
between the river and wetland rice fields because 
the river is narrow in some locations and 
surrounded by bushes and other vegetation. Also 
in some places the wetland rice is located next to 
the river. This causes a mixed signature of water 
and vegetation. Therefore, both were classified 
together. 
Different types of soils (such as red soil, brown 
soil etc.) did not show distinct spectral signatures. 
As a result, they were not separated into different 
classes. The same situation occurred with the 
settlement and homestead gardens. The homestead 
gardens consist of agricultural crops, fruit trees 
and bare soil. The settlements and homestead 
gardens were also classified as one. 
However, it was possible to distinguish between 
natural forest (lowland forest), shifting cultivation 
and secondary forest, wetland rice, settlement, sea 
and lake. The bare soil and beach were classified 
together because both classes have almost the 
same spectral signatures. 
A quantitative evaluation of the supervised 
classification results indicated an overall 
classification performance of the MSS data of 
88.9%, which is relatively good. Most of the 
individual classes had classification accuracies 
exceeding 80 %, except for the bush and grass 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996 
 
	        
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