• Journal of the Korean Wood Science and Technology
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• v.45 no.4
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• pp.369-381
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• 2017

The objective of this study was to compare the wood anatomical characteristics of local tree species in Moluccas, Indonesia i.e., Moluccan ironwood (Intsia bijuga), linggua (Pterocarpus indicus), red meranti (Shorea parvifolia), and gofasa (Vitex cofassus). Qualitative evaluation was conducted by observing the anatomical structure in cross, radial, and tangential sections of each sample. For the quantitative evaluation, the dimensions of vessels, rays, and fibers were measured. Qualitative evaluation showed that crystals were observed in Moluccan ironwood, linggua, and gofasa, while resin canals were only observed in red meranti. Tyloses were frequently observed in gofasa but infrequently observed in linggua and red meranti. Quantitative evaluation showed that Moluccan ironwood with the higher density had thicker fiber wall, higher quantity of ray number, and wider rays than the other species. Red meranti had higher values of ray height and fiber length than the other three species. The results also revealed that linggua showed the highest values of relative crystallinity and crystallite width. Red meranti and gofasa showed similar values of relative crystallinity and crystallite width, while Moluccan ironwood showed the lowest values. The basic qualitative and quantitative anatomical characteristics discussed could provide useful information for further utilizations of such wood species.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.