中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 资源与环境信息系统国家重点实验室
  5. 外文论文
Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

文献类型：期刊论文

作者Dai, Shaoqing5,6,7; Zheng, Xiaoman5,6; Gao, Lei8; Xu, Chengdong2,6; Zuo, Shudi3,5; Chen, Qi1; Wei, Xiaohua4; Ren, Yin3,5
刊名FORESTS
出版日期2021-12-01
卷号12期号:12页码:15
关键词forest aboveground biomass plot-level model machine learning spatial statistical model model combination
DOI10.3390/f12121663
英文摘要Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.
WOS关键词ESTIMATING ABOVEGROUND BIOMASS ; CARBON STOCKS ; PREDICTION ; REGRESSION ; PERFORMANCE ; STRATEGIES ; FRAMEWORK
资助项目National Natural Science Foundation of China[31972951] ; National Natural Science Foundation of China[31670645] ; National Natural Science Foundation of China[42001210] ; National Natural Science Foundation of China[41801182] ; National Natural Science Foundation of China[41807502] ; National Natural Science Foundation of China[41771462] ; National Social Science Fund[17ZDA058] ; National Key Research Program of China[2016YFC0502704] ; Fujian Provincial Department of ST Project[2021I0041] ; Fujian Provincial Department of ST Project[2021T3058] ; Fujian Provincial Department of ST Project[2018T3018] ; Fujian Provincial Department of ST Project[2019J01136] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA23020502] ; Ningbo Municipal Department of Science and Technology[2019C10056] ; Key Laboratory of Urban Environment and Health of CAS[KLUEH-C-201701] ; Key Program of the Chinese Academy of Sciences[KFZDSW-324] ; Fujian Forestry Science and Technology Research Project[MinLinKeBianHan[2020]29]
WOS研究方向Forestry
语种英语
WOS记录号WOS:000742890300001
出版者MDPI
资助机构National Natural Science Foundation of China ; National Social Science Fund ; National Key Research Program of China ; Fujian Provincial Department of ST Project ; Strategic Priority Research Program of Chinese Academy of Sciences ; Ningbo Municipal Department of Science and Technology ; Key Laboratory of Urban Environment and Health of CAS ; Key Program of the Chinese Academy of Sciences ; Fujian Forestry Science and Technology Research Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/169654]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Univ Hawaii, Dept Geog & Environm, Honolulu, HI 96822 USA
2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Ningbo Urban Environm Observat & Res Stn NUEORS, Ningbo 315800, Peoples R China
4.Univ British Columbia, Dept Earth Environm & Geog Sci, Kelowna, BC V1V 1V7, Canada
5.Chinese Acad Sci, Key Lab Urban Environm & Hlth, Fujian Key Lab Watershed Ecol, Key Lab Urban Metab Xiamen,Inst Urban Environm, Xiamen 361021, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
7.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, NL-7500 AE Enschede, Netherlands
8.CSIRO, Waite Campus, Adelaide, SA 5064, Australia
推荐引用方式
GB/T 7714		 Dai, Shaoqing,Zheng, Xiaoman,Gao, Lei,et al. Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics[J]. FORESTS,2021,12(12):15.
APA Dai, Shaoqing.,Zheng, Xiaoman.,Gao, Lei.,Xu, Chengdong.,Zuo, Shudi.,...&Ren, Yin.(2021).Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics.FORESTS,12(12),15.
MLA Dai, Shaoqing,et al."Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics".FORESTS 12.12(2021):15.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。