Satellite Data Classification Accuracy Assessment Based from Reference Dataset
In order to develop forest management strategies in tropical forest in Malaysia, surveying the forest resources and monitoring the forest area affected by logging activities is essential. There are tremendous effort has been done in classification of land cover related to forest resource managem...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English English |
| Published: |
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/7638/1/Satellite%20Data%20Classification%20Accuracy%20Assessment%20Based%20from%20Reference%20Dataset.pdf http://psasir.upm.edu.my/id/eprint/7638/7/Satellite%20Data%20Classification%20Accuracy%20Assessment%20Based%20from%20Reference%20Dataset.pdf http://psasir.upm.edu.my/id/eprint/7638/ http://www.waset.org/journals/ijcise/v2/v2-2-16.pdf |
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| Summary: | In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result. |
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