Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model-Reference-Cited by-同舟云学术

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Sub-meter tree height mapping of California using aerial images and LiDAR-informed U-Net model

Published:2024-05 Issue: Volume:305 Page:114099
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Wagner Fabien H.,Roberts Sophia,Ritz Alison L.,Carter Griffin,Dalagnol Ricardo,Favrichon Samuel,Hirye Mayumi C.M.,Brandt Martin,Ciais Philippe,Saatchi Sassan

Funder

Grantham Foundation for the Protection of the Environment

High Tide Foundation

National Aeronautics and Space Administration

Publisher

Elsevier BV

Reference50 articles.

1. Mapping tropical forest carbon: calibrating plot estimates to a simple lidar metric;Asner;Remote Sens. Environ.,2014

2. Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest;Astola;Remote Sens.,2021

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust characterisation of forest structure from airborne laser scanning—A systematic assessment and sample workflow for ecologists;Methods in Ecology and Evolution;2024-09-06

2. Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data;Remote Sensing of Environment;2024-09

3. Detection of forest disturbance across California using deep-learning on PlanetScope imagery;Frontiers in Remote Sensing;2024-05-31

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