Combining LiDAR Metrics and Sentinel-2 Imagery to Estimate Basal Area and Wood Volume in Complex Forest Environment via Neural Networks
Author:
Affiliation:
1. INRAE, UMR TETIS, University of Montpellier, Montpellier, France
2. University Grenoble Alpes, INRAE, LESSEM, Grenoble, France
Funder
French Agency for Ecological Transition
GRAINE program
French Region of Occitanie
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09779099.pdf?arnumber=9779099
Reference44 articles.
1. Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock
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4. Multisource Remote Sensing Data Classification Based on Convolutional Neural Network
5. Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
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