[1]刘俊,孟雪,温小荣*,等. 基于不同立地质量的森林蓄积量遥感估测[J].西北林学院学报,2016,31(1):186-191.[doi:10.3969/j.issn.1001-7461.2016.01.33]
　LIU Jun,MENG Xue,WEN Xiao-rong*,et al. Remote Sensing Estimation of Forest Volume Based on Different Site Qualities[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2016,31(1):186-191.[doi:10.3969/j.issn.1001-7461.2016.01.33]
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 基于不同立地质量的森林蓄积量遥感估测()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第31卷
期数:
2016年第1期
页码:
186-191
栏目:
出版日期:
2016-01-31

文章信息/Info

Title:
 Remote Sensing Estimation of Forest Volume Based on Different Site Qualities
文章编号:
1001-7461(2016)01-0186-06
作者:
 刘俊1; 2; 孟雪1; 2; 温小荣1; 2*; 林国忠1; 2; 佘光辉1; 2; 刘雪慧1; 2; 徐达3
 （1.南京林业大学 南方现代林业协同创新中心，江苏 南京 210037；2.南京林业大学 林学院，江苏 南京 210037；3.浙江省森林资源监测中心，浙江 杭州 310020）
Author(s):
 LIU Jun1; 2; MENG Xue1; 2; WEN Xiao-rong1; 2*; LIN Guo-zhong1; 2; SHE Guang-hui1; 2; LIU Xue-hui1; 2; XU Da3
 (1.Co-Innovation for Sustainable Forestry in Southern China,Nanjing Forestry University,Nanjing,Jiangsu 210037,China;2.Forestry College of Nanjing Forestry University,Nanjing ,Jiangsu 210037,China; 3.Center for Forest Resource Monitoring of Zhejiang Province,Hangzhou,Zhejiang 310020,China)
关键词:
 ; TM影像; 森林蓄积量; 立地等级; 一元线性回归
Keywords:
 ; TM image;  forest stock volume;  site class;  linear regression
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2016.01.33
文献标志码:
A
摘要:
 森林蓄积量遥感估测在森林资源管理中具有十分重要的意义。以建德市为研究区，利用2007年TM遥感影像和2007年森林资源2类调查数据，对杉木树种分立地质量等级和不分地位等级2种类型建立蓄积量的遥感估测模型，并进行精度检验。其中立地质量等级依据小班平均高和平均年龄建立的地位级表划分为好、中、差3种类型，以每个小班的总蓄积量为因变量，小班各单个遥感因子信息总量为自变量。结果表明：以TM遥感影像主成分分析中第1主成分为自变量的模型拟合效果最好,相关系数R均在0.67以上,最高为0.868；利用预留独立样本对模型精度进行验证，不分地位级总体估测精度为90.31%，分立地质量等级好、中、差3种类型总体的估测精度分别为96.1%、97.24%、95.56%，分立地质量类型建模的精度明显优于统一建模的精度。研究结果为森林蓄积量遥感估测提供一种改进的思路，且为提高森林生物量和碳储量遥感估测精度提供一种参考方法。
Abstract:
 It is very important to estimate forest volume in forestry system.Taking Jiande of Jiangsu Province as the research area,and using TM image ( 2007) and the fifth (2007) forest resource survey data,volume remote sensing estimation models of Cunninghamia lanceolata (Lamb.) Hook.trees with and without site quality grades were established,and the accuracy levels of the models were evaluated.Site quality grades were divided into good,medium and poor three types according to the status table that was established based on average height and the average age of forest subcompartment.Total volume of the sub-compartment is the dependent variable,and each individual remote sensing content was the independent variable.The results showed that:the best fitting effects were achieved by the model in which the first principal component was taken as variable in R Landsat TM image analysis,the average correlation coefficient was more than 0.67,the highest was 0.868.Accuracy test of the models established by reserved independent samples demonstrated that the accuracy levels of the models in which the site quality grades were divided into good,medium and poor (with the accuracy of 96.1%,97.24%,and 96.56%,respectively) was obvious higher than those without the consideration of site quality (90.31%).The research results provide an improved method for the estimation of forest volume,and a reference for improving the accuracy of forest biomass and carbon storage estimation.

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备注/Memo

备注/Memo:
 收稿日期：2015-08-08修回日期：2015-10-13
基金项目：国家948计划项目（2013-4-63）；南京林业大学科技创新基金项目(CX2011-24)；江苏省林业三新工程（LYSX[2015]19)；江苏高校优势学科建设工程自助项目（PAPD)。
作者简介：刘俊，男，在读硕士，研究方向：3S技术与森林资源动态监测。E-mail:daziran2188@163.com
*通信作者：温小荣，男，副教授，研究方向：森林经理及3S技术应用。E-mail:njw9872e@163.com
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