中文摘要：

为提高森林地上生物量估测精度,从建模因子和建模方法出发,提出了一种综合考虑影像纹理特征、地形特征、光谱特征的粒子群优化最小二乘支持向量机生物量估测方法。以松山自然保护区为研究区域,以资源三号遥感卫星数据为数据源,配合194块调查样地实测数据、森林资源二类调查数据、数字高程模型数据,通过分析46个特征变量与森林地上生物量间的Pearson相关性,进行特征变量优化提取,建立PSO-LSSVM模型并在Matlab 2014a上编程实现。以决定系数R2和均方根误差RMSE为指标,对比分析了PSO-LSSVM和多元线性回归地上生物量模型精度。研究结果表明：PSO-LSSVM模型在针叶林、阔叶林、灌木林3种类型中预测决定系数分别为0.867、0.853、0.842,比多元线性回归模型分别提高了23.15%、19.13%、14.40%。PSO-LSSVM地上生物量模型具有良好的自学能力和自适应能力,它取代了传统的遍历优化方法,在全局优化及收敛速度方面具有较大优势,预测精度较高。

英文摘要：

In order to improve the accuracy of forest above-ground biomass estimation,constructed from modeling factor selection and modeling aspects,a PSO-LSSVM biomass estimation method was proposed by considering comprehensive of the image texture features,topographical features,spectral features.Selecting Songshan Nature Reserve as study area,with the data sources from ZY-3 satellite remote sensing image,the measured data of 194 survey plots,forest resource inventory data,and the digital elevation model data,the Pearson correlation relationship was analyzed between 46 feature variables and forest above-ground biomass. With the optimal feature extraction variables chosen,the PSO-LSSVM model was established in Matlab 2014 a. The determination coefficient（ R~2） and root mean square error（ RMSE）were taken for comparative analysis of the accuracy of PSO-LSSVM model and multiple linear regression model. The results showed that the prediction accuracies（ R2） of PSO-LSSVM model in coniferous forest,broadleaf forest and shrub were 0. 867,0. 853 and 0. 842,which were improved by 23. 15%,19. 13% and 14. 40% compared with the multiple linear regression model,respectively. The PSOLSSVM model had self-study ability and adaptive capability,it can replace the traditional traversal optimization method,and it had great advantages on global optimization and convergence rate with smallsample volume requirement and high precision accuracy.