Assessment of Microw Ave Remote Sensing Technology (Nasa's Airsar-Topsar Data) for Forest Type Classification on Tioman Island, Pahang, Malaysia
Active microwave remote sensing is able to provide information about land surface and forest canopy that would otherwise be unobtainable in regions where cloud cover and darkness prevail. The general objective of this study is to assess the capability and applicability of NASA's airborne SAR...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2000
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/10001/1/FH_2000_1_IR.pdf |
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| Summary: | Active microwave remote sensing is able to provide information about land
surface and forest canopy that would otherwise be unobtainable in regions where
cloud cover and darkness prevail. The general objective of this study is to assess the
capability and applicability of NASA's airborne SAR (AirSAR) data to classify and
map tropical forests utilizing the Environment for Visualizing Images (ENVI) image
processing software. The specific objectives are to test the applicability of TOPSAR
in classifying forest types of Tioman Island applying standard classification method,
generate a digital topographic map, generate a DEM of the study site and produce a
forest-type map of Tioman Island. The island is approximately 13,354 hectares.
AirSAR's Topographic SAR (TOPSAR) data of the entire island comprising of two
strips were acquired on a 3rd December 1996 flight mission. Prior to analysis, the
image had to be despeckled to remove noise. Five adaptive filters were used and the
Gamma filter with an 11x11 window produced the best visual results after initially
applying image contrast stretching. Preliminary ground survey revealed that the
island has at least five main vegetation types identified as primary forest, beach
forest, secondary forest, coconut palm plantation and mangrove forest. Two methods
of interpretation were applied. In the first method, visual interpretation was initially applied where distinct different tones and texture were designated as a "Region of
Interest" (ROI) for signature extraction. 16 ROIs were created to represent four
vegetation covers and polarization signatures for each ROI were generated. Extracted
polarization signatures showed no specific signature or pattern for a particular known
forest type. The second method, unsupervised classification, initially yielded 10
classification classes. However, only two land cover classes were readily
distinguished and these could be classified as primary and secondary forests. An
additional classification obtained is cleared land or developed land. It is therefore
suggested that fully Polarimetric SAR data be used together with the TOPSAR. |
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