[An automatic extraction algorithm for individual tree crown projection area and volume based on 3D point cloud data].

Tree crown projection area and crown volume are the important parameters for the estimation of biomass, tridimensional green biomass and other forestry science applications. Using conventional measurements of tree crown projection area and crown volume will produce a large area of errors in the view of practical situations referring to complicated tree crown structures or different morphological characteristics. However, it is difficult to measure and validate their accuracy through conventional measurement methods. In view of practical problems which include complicated tree crown structure, different morphological characteristics, so as to implement the objective that tree crown projection and crown volume can be extracted by computer program automatically. This paper proposes an automatic untouched measurement based on terrestrial three-dimensional laser scanner named FARO Photon120 using plane scattered data point convex hull algorithm and slice segmentation and accumulation algorithm to calculate the tree crown projection area. It is exploited on VC+6.0 and Matlab7.0. The experiments are exploited on 22 common tree species of Beijing, China. The results show that the correlation coefficient of the crown projection between Av calculated by new method and conventional method A4 reaches 0.964 (p<0.01); and the correlation coefficient of tree crown volume between V(VC) derived from new method and V(C) by the formula of a regular body is 0.960 (p<0.001). The results also show that the average of V(C) is smaller than that of V(VC) at the rate of 8.03%, and the average of A4 is larger than that of A(V) at the rate of 25.5%. Assumed Av and V(VC) as ture values, the deviations of the new method could be attributed to irregularity of the crowns' silhouettes. Different morphological characteristics of tree crown led to measurement error in forest simple plot survey. Based on the results, the paper proposes that: (1) the use of eight-point or sixteen-point projection with fixed angles to estimate crown projections, and (2) different regular volume formula to simulate crown volume according to the tree crown shapes. Based on the high-resolution 3D LIDAR point cloud data of individual tree, tree crown structure was reconstructed at a high rate of speed with high accuracy, and crown projection and volume of individual tree were extracted by this automatical untouched method, which can provide a reference for tree crown structure studies and be worth to popularize in the field of precision forestry.

[Research on living tree volume forecast based on PSO embedding SVM].

In order to establish volume model,living trees have to be fallen and be divided into many sections, which is a kind of destructive experiment. So hundreds of thousands of trees have been fallen down each year in China. To solve this problem, a new method called living tree volume accurate measurement without falling tree was proposed in the present paper. In the method, new measuring methods and calculation ways are used by using photoelectric theodolite and auxiliary artificial measurement. The diameter at breast height and diameter at ground was measured manually, and diameters at other heights were obtained by photoelectric theodolite. Tree volume and height of each tree was calculated by a special software that was programmed by the authors. Zhonglin aspens No. 107 were selected as experiment object, and 400 data records were obtained. Based on these data, a nonlinear intelligent living tree volume prediction model with Particle Swarm Optimization algorithm based on support vector machines (PSO-SVM) was established. Three hundred data records including tree height and diameter at breast height were randomly selected form a total of 400 data records as input data, tree volume as output data, using PSO-SVM tool box of Matlab7.11, thus a tree volume model was obtained. One hundred data records were used to test the volume model. The results show that the complex correlation coefficient (R2) between predicted and measured values is 0. 91, which is 2% higher than the value calculated by classic Spurr binary volume model, and the mean absolute error rates were reduced by 0.44%. Compared with Spurr binary volume model, PSO-SVM model has self-learning and self-adaption ability,moreover, with the characteristics of high prediction accuracy, fast learning speed,and a small sample size requirement, PSO-SVM model with well prospect is worth popularization and application.