1. Institute of GIS, RS & GPS, Beijing Forestry University, Beijing 100083, China 2. Beijing Vocational College of Agriculture，Beijing 102442, China 3. Water Conservancy and Civil Engineering, China Agricultural University, Beijing 100083, China 4. College of Computer and Information Engineering, Southwest Forestry University, Kunming 650224, China
Abstract：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.
Key words：Particle swarm optimization(PSO);Support vector machine(SVM);Living tree volume;Photoelectric theodolite
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