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Johor Bahru, Malaysia

Chew W.C.,TropicalMap Research Group | Lau A.M.S.,TropicalMap Research Group | Kanniah K.D.,TropicalMap Research Group
International Journal of Geoinformatics

High diversity of tree species in tropical forest is a constraint to achieve satisfactory accuracy in tree species classification, as accuracy reduces with the increasing of target tree species. A new multi-level adaptive classification procedure is introduced in the present study employing Support Vector Machine (SVM). The experiment handled 20 tropical tree species classification using in-situ hyperspectral data. Three levels of classification were carried out and the final overall classification accuracy was improved to 74.56% from the beginning accuracy produced by SVM itself Result of SVM also has proven its better capability than Maximum Likelihood Classification (MLC) in tropical tree species classification. © Geoinformatics International. Source

Rahman M.Z.A.,TropicalMap Research Group | Majid Z.,University of Technology Malaysia | Abu Bakar M.A.,TropicalMap Research Group | Rasib A.W.,TropicalMap Research Group | Kadir W.H.W.,TropicalMap Research Group
Jurnal Teknologi

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models. © 2015 Penerbit UTM Press. All rights reserved. Source

Jamaluddin S.,TropicalMap Research Group | Rasib A.W.,TropicalMap Research Group | Abd Rahman M.Z.,TropicalMap Research Group | Ibrahim A.L.,TropicalMap Research Group | And 3 more authors.
34th Asian Conference on Remote Sensing 2013, ACRS 2013

Tree height is one of essential components in study of forest biomass. Practically, tree height has been direct calculated based on established allometric equation after regular census of diameter of breast height (DBH). However, DBH measurements in high density of primary tropical rainforest is time consuming and laborious. Indeed, over high density vegetated area such as in primary tropical rainforest, advances technology is necessary in order to support the limitation of the sustainability process. Thus, this study shows the use of airbone terrestrial elevation model from IfSAR sensor to derive the tree height in oldest and high density tropical rain forest reserve at nearly center of Peninsular Malaysia named as Pasoh Forest Reserve (PFR). Analysis from random of tree height samples derived from IfSAR gives a good confidence accuracy (RMSE = 1.64) and highly correlated (r2 = 0.6) compare with biometric tree height (calculated using established biometric equation). Source

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