中文摘要：

以香格里拉县高山松为研究对象，利用2006年香格里拉县TM遥感影像、2006年森林资源二类调查小班数据、2009年精度为30 m 的DEM数据以及2013年香格里拉县高山松实测样地数据，提取研究区内高山松林影像分布图及筛选出17个因子（13个遥感因子、3个地形因子、1个地面调查因子）作为备选自变量，在MAT-LAB下利用LIBSVM模块建立研究区高山松林蓄积量单位面积（30 m ×30 m）估测模型。结果表明，选用RBF核函数在参数范围内寻找出SVM模型的最佳参数C＝3.5809， g＝0.1、 p＝0.01，利用最佳寻优参数建立SVM非参数模型，对SVM模型进行测试得到，均方根误差MSE＝0.0087，复相关系数R＝0.51，相对误差RE＝23.4％，估测精度为76.6％。以像元为单位，分块提取高山松林对应的各像元自变量因子，利用估测模型预测得到香格里拉县高山松林总蓄积量为13318476.5 m3。

英文摘要：

By taking the Pinus densata in Shangri-La County as the research target , TM remote sensing image of Shangri-La County in 2006 , the forest resources inventory data in 2006 , the DEM data of 30 meters precision in 2009 , and the Pinus densata ’ s ground sample data of Shangri-La County in 2013 was adopted as the data source.The Pinus densata’s distribution image in the study area was extracted , and 17 factors （13 remote sensing factors, 3 terrain factors, 1 ground survey factors ） was selected as the alternative variables.By using LIBSVM module in MATLAB, the estimation model of Pinus densata’ s per unit （30 m ×30 m ） stock volume of study area was estab-lished.The results showed that the best optimal parameters were C =3.580 9, g=0.1, p=0.01 by using the RBF kernel function in the range of constant , MSE=0.008 7, R=0.51, RE=23.4%, in SVM model test with esti-mation accuracy of 76.6 %, and the predicted total volume of Pinus densata was 13 318 476.5 m3 in Shangri-La county by taking the pixel as unit and extracting the independent variable factors.