Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest

Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously inc...

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Bibliographic Details
Main Authors: Heikki Astola, Lauri Seitsonen, Eelis Halme, Matthieu Molinier, Anne Lönnqvist
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Remote Sensing
Subjects:
deep learning
deep neural networks
transfer learning
sentinel-2
boreal forest
forest variables
Online Access:https://www.mdpi.com/2072-4292/13/12/2392

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