Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms

Aboveground biomass estimation of black locust planted forests with aspect variable using machine learning regression algorithms

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting and afforestation policy making, and the aspect factors that affect forest stand growth are important to the accuracy of AGB estimation. In this study, aspect was incorporated as a variable into three machi...

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Bibliographic Details
Main Authors: Liu, J. (Author), Ye, Q. (Author), Yu, S. (Author), Zhao, Q. (Author), Zhao, Z. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
Above ground biomass
aboveground biomass
Aboveground biomass
Accurate estimation
algorithm
artificial neural network
Aspect
Biomass
Biomass estimation
Black locust
Black locust planted forest
Black locust planted forests
deciduous tree
Decision trees
detection method
dicotyledon
Ecology
forest ecosystem
Learning algorithms
machine learning
Machine learning algorithms
Machine-learning
Neural networks
Random forests
Reforestation
Regression algorithms
Robinia pseudoacacia
Support vector machines
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