杨柳,孙金华,冯仲科,岳德鹏,杨立岩.基于PSO-LSSVM的森林地上生物量估测模型[J].农业机械学报,2016,47(8):273-279,287.
Yang Liu,Sun Jinhua,Feng Zhongke,Yue Depeng,Yang Liyan.Estimation Model of Forest Above-ground Biomass Based on PSO-LSSVM[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(8):273-279,287.
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基于PSO-LSSVM的森林地上生物量估测模型   [下载全文]
Estimation Model of Forest Above-ground Biomass Based on PSO-LSSVM   [Download Pdf][in English]
投稿时间:2016-03-29  
DOI:10.6041/j.issn.1000-1298.2016.08.036
中文关键词:  森林地上生物量  粒子群算法  最小二乘支持向量机  估测模型
基金项目:国家自然科学基金项目(41371001)、北京市科技专项项目(Z15110000161596)、北京林业大学青年教师科学研究中长期项目(2015ZCQ-LX-01)和平顶山学院青年科研基金项目(PDSU-QNJJ-2013007)
作者单位
杨柳 北京林业大学
平顶山学院 
孙金华 中国矿业大学(北京) 
冯仲科 北京林业大学 
岳德鹏 北京林业大学 
杨立岩 北京林业大学 
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R 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。
Yang Liu  Sun Jinhua  Feng Zhongke  Yue Depeng  Yang Liyan
Beijing Forestry University;Pingdingshan University,China University of Mining and Techology,Beijing Forestry University,Beijing Forestry University and Beijing Forestry University
Key Words:forest above-ground biomass  PSO  LSSVM  estimation model
Abstract: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 2014a. The determination coefficient (R) 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 (R) 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 PSO-LSSVM 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 small sample volume requirement and high precision accuracy.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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