[1]陈蜀蓉,张 超,郑超超,等.公益林生物量估算方法研究[J].浙江林业科技,2015,35(05):20-28.
　CHEN Shu-rong,ZHANG Chao,ZHENG Chao-chao,et al.Estimation Methods for Biomass of Ecological Forest in Jinyun[J].Journal of Zhejiang Forestry Science and Technology,2015,35(05):20-28.
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公益林生物量估算方法研究()
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《浙江林业科技》[ISSN:1001-3776/CN:33-1112/S]

卷:
35
期数:
2015年05期
页码:
20-28
栏目:
出版日期:
2015-09-30

文章信息/Info

Title:
Estimation Methods for Biomass of Ecological Forest in Jinyun
文章编号:
1001-3776（2015）05-0020-09
作者:
陈蜀蓉1; 张 超1; 郑超超1; 张 伟1; 伊力塔1; 2; 余树全1; 2*
 (1. 浙江农林大学林业与生物技术学院,浙江 临安 311300;2. 亚热带森林培育国家重点实验室培育基地,浙江 临安 311300)
Author(s):
CHEN Shu-rong1; ZHANG Chao1; ZHENG Chao-chao1; ZHANG Wei1YI Li-ta1; 2; YU Shu-quan1; 2*
(1. School of Forestry and Biotechnology, Zhejiang A & F University, Linan 311300, China; 2. The Nurturing Station for the State Key Laboratory of Subtropical Siliviculture, Linan 311300, China)
关键词:
生物量; 多元线性回归; 偏最小二乘回归; 随机森林; Erf-BP神经网络
Keywords:
biomass;  PLS regression;  PLS-Bootstrap regression;  random forest regression;  BP neutral network model based on Gaussian error function (Erf-BP)
分类号:
S718.55
文献标志码:
A
摘要:
以缙云县公益林为例，利用2010年的117个公益林固定小班监测数据和Landsat5 TM遥感数据，选取遥感变量和地学变量等80个自变量，运用多元线性回归、偏最小二乘回归、随机森林回归和Erf-BP神经网络四种模型，对缙云县公益林生物量进行建模估算，并比较四种方法的优缺点。结果表明：在R2、PRECISION和RMSE方面，随机森林回归优于其他方法，而在VR和BIAS方面，Erf-BP神经网络方法比其他方法更好，但从提高生物量精度和减少均方根误差综合评价，随机森林方法是较好的选择。
Abstract:
Biomass of ecological forest in Jinyun county, Zhejiang province was estimated by multiple linear regression (MLR), partial least squares(PLS) regression, random forest regression and BP neutral network model based on Gaussian error function (Erf-BP), according to data from TM imagery and 117 permanent subcompartments forest management survey in 2010. There were 80 independent variables of geoscience and remote sensing. Results showed that random forest regression had better effect on R2, PRECISION and RMSE, while Erf-BP neural network on VR and BIAS. Comprehensive evaluation on precision and root mean square error indicated that random forest method was a better choice

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备注/Memo

备注/Memo:
基金项目：浙江省重点科技创新团队项目（2011R50027）
更新日期/Last Update: 2016-03-26