Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms-Reference-Cited by-同舟云学术

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Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms

Published:2018-06 Issue: Volume:256-257 Page:445-457
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Li Xuejian,Du Huaqiang,Mao Fangjie,Zhou Guomo,Chen Liang,Xing Luqi,Fan Weiliang,Xu Xiaojun,Liu Yuli,Cui Lu,Li Yangguang,Zhu Dien,Liu Tengyan

Funder

National Natural Science Foundation

Natural Science Foundation of Zhejiang Province

Joint Research fund of Department of Forestry of Zhejiang Province and Chinese Academy of Forestry

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

Reference52 articles.

1. Capabilities and limitations of landsat and land cover data for aboveground woody biomass estimation of Uganda;Avitabile;Remote Sens. Environ.,2012

2. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information;Blackard;Remote Sens. Environ.,2008

3. Random forests;Breiman;Mach. Learn.,2001

4. Defining leaf area index for non-flat leaves;Chen;Plant Cell Environ.,1992

5. Random forests for classification in ecology;Cutler;Ecology,2007

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