Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements

Approximating the complex nonlinear relationships that dominate the exchange of carbon dioxide fluxes between the biosphere and atmosphere is fundamentally important for addressing the issue of climate change. The progress of machine learning techniques has offered a number of useful tools for the s...

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Bibliographic Details
Main Authors: Xianming Dou, Yongguo Yang, Jinhui Luo
Format: Article
Language:English
Published: MDPI AG 2018-01-01
Series:Sustainability
Subjects:
carbon fluxes
boreal forests
machine learning
eddy covariance
adaptive neuro-fuzzy inference system
generalized regression neural network
Online Access:http://www.mdpi.com/2071-1050/10/1/203

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