Modelling forest tree volumehas a long history due to its importance in forest management decision making. However, tree biomass prediction become more popular recently because it has a strong relationship with carbon sequestration. Over the years, numerous attempts were made to construct allometric models in predicting tree volume and biomass in Sri Lanka for different forest species. Volume and biomass estimation in forest trees in Sri Lanka can be divided into four main types, i.e., (i) use of specific models built for the target species, (ii) use of models originally built for different tree species from the target once, (iii) use of common/universal conversions and (iv) use of remote sensing related studies. The first three types, however, became more common because mainly remote sensing studies do not facilitate the biomass estimation at the tree level. Details of tree volume and biomass prediction models constructed for Eucalyptus grandis, E.torelliana, E. microcorys, Tectona grandis, Pinus caribaea, Khaya senegalensis and Alstonia macrophylla are discussed in this paper. Moreover, it discusses the result of a study conducted in a wet zone natural forest to predict species-specific individual tree biomass using diameter as the only explanatory variable. Finally it elaborates the issues faced in developing allometric equations in Sri Lanka.
Detection of corners is an important task in computer vision to capture discontinuous boundaries of objects of interest. Present operators designed to detect boundaries having sharp corners often produce unsatisfactory results because the points detected can also be an isolated point, ending of a thin line or a maximum curvature region of a planar curve. A novel corner detection operator, capable of detecting corner points that exist only on the boundary of an object, is presented in this paper. Initially, candidate corner points are detected by exploiting intensity information of the local neighborhood and associated connectivity pattern around the center point within a local window. Further verification is done to confirm whether the detected corner point is on the boundary of the targeted object. As the proposed operator is isotropic, it covers all the orientations and corner angles by performing a single computation step within the local window. The performance of the operator is tested with both synthetic and real images and the results are compared with other major corner detectors