Forest classification and mapping for resource management at the Gunung Stong Forest Reserve, Peninsular Malaysia
Forest is the main natural resources and heritage of the country. Apart from functioning to maintain biological diversity, forest is also a country economic generator, i.e., as the major supplier of world timber. Geographical structure of hills and tropical climate in Peninsular Malaysia create a cr...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/49330/1/FH%202012%2026RR.pdf |
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| Summary: | Forest is the main natural resources and heritage of the country. Apart from functioning to maintain biological diversity, forest is also a country economic generator, i.e., as the major supplier of world timber. Geographical structure of hills and tropical climate in Peninsular Malaysia create a critical phenomenon on mobilizing human resources for identification of forest species biogeography. The most suitable technique to overcome
this problem is to apply remote sensing technology for inventory, classifying and mapping of forest resources. By classifying and mapping forest tree species it will be
useful to develop and manage forest resource in sustainable manner. The main objective of this study is to investigate the capabilities of hyperspectral data on managing tropical
forest information in Gunung Stong Forest Reserve, Kelantan, Peninsular Malaysia. Field spectroradiometer instrument is used in the field to develop the spectral curve. Hyperspectral with spectral reflection data (288 bands, 500-850nm) is obtained based on the existing tree canopy which stands out from the image. Analysis is performed on the study plot with size of five (5) hectares. Spectral properties of each species were taken.
Classification of species was carried out based on Spectral Angle Mapper (SAM) classification technique. Eight species of forest trees have been identified, i.e., Chengal
(Neobalanocarpus hemii), Kembang Semangkok (Scaphium macropodum), Kekatong (Cynometra malaccensis), Gerutu (Parashorea spp.), Meranti Kepong (Shorea ovalis),
Kasai (Pometia pinnata), Merpauh (Swintonia spp.), and Merawan (Hopea spp.). The mapping accuracy was 79%. The distribution of Kekatong species is the greatest, i.e.
45.61%. The analysis of tree canopy and volume shows an estimated of 16,236.00m2 trees canopies. A total of 318.37m3 is the volume of trees. The study concluded that by applying remote sensing technology and the use of high resolution hyperspectral for classification of forest species and mapping, it can help towards implementing
sustainable forest management through the implementation of Malaysian Criteria and Indicator (MC&I). |
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