[1]刘慧婷,潘俊*,符玥,等. 不同植被类型森林生物量反演模型研究——以湖南省公益林为例[J].西北林学院学报,2024,39(1):73-80.[doi:10.3969/j.issn.1001-7461.2024.01.10]
　LIU Hui-ting,PAN Jun*,FU Yue,et al. Inversion Models of Forest Biomass for Different Vegetation Types——A Case Study of Public Welfare Forests in Hunan Province[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2024,39(1):73-80.[doi:10.3969/j.issn.1001-7461.2024.01.10]
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 不同植被类型森林生物量反演模型研究——以湖南省公益林为例()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第39卷
期数:
2024年第1期
页码:
73-80
栏目:
出版日期:
2024-01-31

文章信息/Info

Title:
 Inversion Models of Forest Biomass for Different Vegetation Types——A Case Study of Public Welfare Forests in Hunan Province
文章编号:
1001-7461(2024)01-0073-08
作者:
 刘慧婷1; 2; 潘俊3*; 符玥4; 王光军4; 樊红波1; 2; 胡孔飞5
 （1.核工业二三○研究所，湖南 长沙 410007；2.湖南省伴生放射性矿产资源评价与综合利用工程技术研究中心，湖南 长沙 410007；3.中南林业科技大学 理学院，湖南 长沙 410004；4.中南林业科技大学 生命科学与技术学院，湖南 长沙 410004；5.湖南景辉农林生态科技有限公司，湖南 长沙 410004）
Author(s):
 LIU Hui-ting1; 2; PAN Jun3*; FU Yue4; WANG Guang-jun4; FAN Hong-bo1; 2; HU Kong-fei5
 (1.Changsha Uranium Geology Research Institute,Changsha 410007,Hunan,China；2.Hunan Engineering Technology Research Center for Evaluation and Comprehensive Utilization of Associated Radioactive Mineral Resources,Changsha 410007,Hunan,China；3.College of Science,Central South University of Forestry and Technology,Changsha 410004,Hunan,China)
关键词:
 ; 植被类型; 生物量; Landsat 8 OLI; 机器学习; 湖南省公益林
Keywords:
 ; vegetation type;  biomass;  Landsat 8 OLI;  machine learning;  Hunan public welfare forest
分类号:
S718.556
DOI:
10.3969/j.issn.1001-7461.2024.01.10
文献标志码:
A
摘要:
 生态公益林是构筑国土生态安全的重要基础，是实现“两山”理念的重要保障。以湖南省不同植被类型（针叶林、阔叶林、针阔混交林、竹林和灌木）公益林为对象，利用2021年湖南省公益林固定样地监测数据、Landsat 8遥感数据，构建不同植被类型公益林生物量支持向量机模型、决策树模型和随机森林模型3种反演模型。结果表明，3种模型中，随机森林模型估算精度最高，其中竹林的拟合程度最好，R2为0.79，RMSE为25.60 t·hm-2。研究结果表明，基于随机森林模型、植被类型分类反演可以有效提高森林生物量的估测精度，为提升森林生物量估算的准确性提供新方法。
Abstract:
 Ecological public welfare forest is an important foundation for building national ecological security and an important guarantee for implementing the “Two Mountains Theory”.In this study,public welfare forests with different vegetation types (coniferous forests,broad-leaved forests,mixed forests of coniferous and broad-leaved trees,bamboo forests,and shrubs) occurring in Hunan Province were selected as research objects.By using the fixed plot monitoring data of public welfare forests in Hunan Province and Landsat 8 remote sensing data in 2021,three inversion models,including the biomass support vector machine model,decision tree model,and random forest model,were constructed for public welfare forests with different vegetation types.The results showed that among the three models,the random forest model had the highest estimation accuracy,with the best fit for bamboo forests (R2:0.79 and RMSE:25.60 t·hm-2).The research results confirmed that vegetation classification inversion based on the random forest model could effectively improve the estimation accuracy of forest biomass and provide a new method for improving the accuracy of forest biomass estimation.

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备注/Memo

备注/Memo:
 收稿日期：2022-11-01修回日期：2023-04-21
基金项目：湖南省重点研发项目(2022NK2018)；广西壮族自治区科技攻关计划项目(桂科AB21220026)。
第一作者：刘慧婷。研究方向:生物技术与工程。E-mail:1553990298@qq.com
*通信作者：潘俊,博士,讲师。研究方向:最优化方法系统工程。E-mail:1228272980@qq.com
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