Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data-Reference-Cited by-同舟云学术

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Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data

Published:2024-09 Issue: Volume:311 Page:114270
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Carcereri Daniel^ORCID,Rizzoli Paola^ORCID,Dell’Amore Luca,Bueso-Bello José-Luis^ORCID,Ienco Dino^ORCID,Bruzzone Lorenzo^ORCID

Publisher

Elsevier BV

Reference42 articles.

1. Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles;Becker;ISPRS J. Photogramm. Remote Sens.,2023

2. The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography;Blair;ISPRS J. Photogramm. Remote Sens.,1999

3. A deep learning framework for the estimation of forest height from bistatic TanDEM-X data;Carcereri;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2023

4. Forest canopy height estimation using Tandem-X coherence data;Chen;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2016

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