Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data

Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data

Abstract Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these images often have missing data in tropical regions due to climate driven persistent clo...

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Bibliographic Details
Main Authors: Jacinta Holloway-Brown, Kate J Helmstedt, Kerrie L Mengersen
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:Journal of Big Data
Subjects:
Random forest
Uncertainty
Stochastic
Machine learning
Spatial interpolation
Remote sensing
Online Access:http://link.springer.com/article/10.1186/s40537-020-00331-8

Internet

http://link.springer.com/article/10.1186/s40537-020-00331-8

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