Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests-Reference-Cited by-同舟云学术

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Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests

Published:2020-06 Issue: Volume:466 Page:118104
ISSN:0378-1127
Container-title:Forest Ecology and Management
language:en
Short-container-title:Forest Ecology and Management

Author:

Chen Jiaxin,Yang Hongqiang,Man Rongzhou,Wang Weifeng,Sharma Mahadev,Peng Changhui,Parton John,Zhu Huaiping,Deng Ziwang

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Nature and Landscape Conservation,Forestry

Reference62 articles.

1. A novel modelling approach for predicting forest growth and yield under climate change;Ashraf;PlusOne,2015

2. Machine learning for data driven discovery in solid Earth geoscience;Bergen;Science,2019

3. Evaluation of nonlinear equations for predicting diameter from tree height;Bi;Can. J. For. Res.,2012

4. Observing climate change impacts on European forests: what works and what does not in ongoing long-term monitoring networks;Bussotti;Front. Plant Sci.,2017

5. The relative growth rates of white spruce and balsam fir trees in the Port Arthur District of Ontario;Burgar;For. Chron.,1961

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