[1]胥喆,舒清态*,杨凯博,等. 基于非成像高光谱的高山松叶绿素估算模型研究[J].西北林学院学报,2017,32(2):73-78.[doi:10.3969/j.issn.1001-7461.2017.02.12]
　XU Zhe,SHU Qing-tai*,YANG Kai-bo,et al. Estimation Models of Chlorophyll in Pinus densata Based on Non-imaging Hyperspectrum[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2017,32(2):73-78.[doi:10.3969/j.issn.1001-7461.2017.02.12]
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 基于非成像高光谱的高山松叶绿素估算模型研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第32卷
期数:
2017年第2期
页码:
73-78
栏目:
出版日期:
2017-03-31

文章信息/Info

Title:
 Estimation Models of Chlorophyll in Pinus densata Based on Non-imaging Hyperspectrum
文章编号:
1001-7461(2017)02-0073-06
作者:
 胥喆; 舒清态*; 杨凯博; 吴娇娇; 张亚红
 （西南林业大学 林学院，云南 昆明 650224）
Author(s):
 XU Zhe; SHU Qing-tai*; YANG Kai-bo; WU Jiao-jiao; ZHANG Ya-hong
 (College of Forestry,Southwest Forestry University,Kunming,Yunnan 650224,China)
关键词:
 ; 高光谱; 叶绿素; BP神经网络; 相关系数; 高山松
Keywords:
 ; hyperspectrum;  chlorophyll;  BP neural network;  correlation coefficient;  Pinus densata
分类号:
S757
DOI:
10.3969/j.issn.1001-7461.2017.02.12
文献标志码:
A
摘要:
 以云南省香格里拉县建塘镇的高山松为研究对象，使用ASD Field Spec 3便携式野外地物光谱仪测定高山松叶片光谱，并在实验室测定叶片样本的叶绿素含量。经光谱分析技术及统计相关分析法进行光谱数据的分析处理，提取原始光谱及一阶微分光谱特征波段和光谱特征变量，建立与叶绿素含量间的单变量估测模型和双隐层BP神经网络预测模型，并采用决定系数(R2)、均方根差(RMSE)和相对误差(RE)进行精度检验。结果表明，单变量模型以一阶微分光谱反射率的三次函数模型为最优模型，其R2、RMSE、RE分别为0.511、1.297 6 mg/g、10.06%，而基于双隐层BP神经网络最优模型的R2、RMSE、RE分别为0.637、0.384 1 mg/g、9.47%，精度达到90.53%，经比较得出其具有较优的预测能力，充分体现BP模型的可行性，为快速、准确的估测高山松叶绿素含量提供有利的理论依据。
Abstract:
Pinus densata occurring in Jiantang township of Shangrila,Yunnan Province was taken as the research object,the spectral reflectance of the leaves were measured by a portable field surface object spectrometer (model ASD Field Spec 3),while the content of chlorophyll in the leaves were measure in laboratory.The spectral data were processed using spectral analysis techniques and statistical methods to extract the original spectrum,differential spectral and spectral characteristic variables,which were then adopted to establish the single variable estimation models and the double hidden layer BP neural network prediction model estimate chlorophyll content.The fitness between the predicted values and the measured values were evaluated by the determination coefficient (R2),the root mean-square error (RMSE) and the average relative error (RE).The results showed that the best single variable model was the cubic model using the first order differential spectral reflectance as a variable,in which the values of R2,RMSE and RE were 0.511,1.297 6 mg/g and 10.06%,respectively.The values of R2,RMSE and RE of the best double hidden layer BP model were 0.637,0.3841 mg/g and 9.47%,respectively,in which the prediction accuracy was 90.53%,indicating it had better predictive ability,and could provide a rapid and accurate way to estimate the content of P.densata chlorophyll leaves.

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备注/Memo

备注/Memo:
 收稿日期：2016-06-26修回日期：2016-10-10
基金项目：国家自然科学基金(31460194)；国家自然科学基金(31060114)；云南省林学一流学科建设项目。
作者简介：胥喆，男，在读硕士，研究方向：3S技术在林业中的应用。E-mail:987348582@qq.com
*通信作者：舒清态，男，副教授，博士，研究方向：3S技术及森林景观经营。E-mail:shuqt@163.com
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