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Canopy height and biomass distribution across the forests of Iberian Peninsula

Su, Yang ; Schwartz, Martin ; Fayad, Ibrahim ; García, Mariano ; Zavala, Miguel A. ; Tijerín-Triviño, Julián ; Astigarraga, Julen LU orcid ; Cruz-Alonso, Verónica ; Liu, Siyu LU and Zhang, Xianglin , et al. (2025) In Scientific Data 12(1).
Abstract

Accurate mapping of vegetation canopy height and biomass distribution is essential for effective forest monitoring, climate change mitigation, and sustainable forestry. Here we present high-resolution remote sensing-based canopy height (10 m resolution) and above ground biomass (AGB, 50 m resolution) maps for the forests of the Iberian Peninsula from 2017 to 2021, using a deep learning framework that integrates Sentinel-1, Sentinel-2, and LiDAR data. Two UNET models were developed: one trained on Airborne Laser Scanning (ALS) data (MAE: 1.22 m), while another using Global Ecosystem Dynamics Investigation (GEDI) footprints (MAE: 3.24 m). External validation with 6,308 Spanish National Forest Inventory (NFI) plots (2017-2019) confirmed... (More)

Accurate mapping of vegetation canopy height and biomass distribution is essential for effective forest monitoring, climate change mitigation, and sustainable forestry. Here we present high-resolution remote sensing-based canopy height (10 m resolution) and above ground biomass (AGB, 50 m resolution) maps for the forests of the Iberian Peninsula from 2017 to 2021, using a deep learning framework that integrates Sentinel-1, Sentinel-2, and LiDAR data. Two UNET models were developed: one trained on Airborne Laser Scanning (ALS) data (MAE: 1.22 m), while another using Global Ecosystem Dynamics Investigation (GEDI) footprints (MAE: 3.24 m). External validation with 6,308 Spanish National Forest Inventory (NFI) plots (2017-2019) confirmed canopy height reliability, showing MAEs of 2-3 m in tree-covered areas. AGB estimates were obtained through Random Forest models that linked UNET derived height predictions to NFI AGB data, achieves an MAE of ~29 Mg/ha. The creation of high-resolution maps of canopy height and biomass across various forest landscapes in the Iberian Peninsula provides a valuable new tool for environmental researchers, policy makers, and forest management professionals, offering detailed insights that can inform conservation strategies, carbon sequestration efforts, and sustainable forest management practices.

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publishing date
type
Contribution to journal
publication status
published
keywords
Forests, Biomass, Spain, Remote Sensing Technology, Trees, Ecosystem, Climate Change, Deep Learning
in
Scientific Data
volume
12
issue
1
article number
678
pages
12 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:105003820733
  • pmid:40263468
  • pmid:40263468
ISSN
2052-4463
DOI
10.1038/s41597-025-05021-9
language
English
LU publication?
no
additional info
© 2025. The Author(s).
id
dcc3ed2e-95e4-4128-9216-a5a1c3598aef
date added to LUP
2025-04-29 14:16:34
date last changed
2025-07-09 17:00:08
@article{dcc3ed2e-95e4-4128-9216-a5a1c3598aef,
  abstract     = {{<p>Accurate mapping of vegetation canopy height and biomass distribution is essential for effective forest monitoring, climate change mitigation, and sustainable forestry. Here we present high-resolution remote sensing-based canopy height (10 m resolution) and above ground biomass (AGB, 50 m resolution) maps for the forests of the Iberian Peninsula from 2017 to 2021, using a deep learning framework that integrates Sentinel-1, Sentinel-2, and LiDAR data. Two UNET models were developed: one trained on Airborne Laser Scanning (ALS) data (MAE: 1.22 m), while another using Global Ecosystem Dynamics Investigation (GEDI) footprints (MAE: 3.24 m). External validation with 6,308 Spanish National Forest Inventory (NFI) plots (2017-2019) confirmed canopy height reliability, showing MAEs of 2-3 m in tree-covered areas. AGB estimates were obtained through Random Forest models that linked UNET derived height predictions to NFI AGB data, achieves an MAE of ~29 Mg/ha. The creation of high-resolution maps of canopy height and biomass across various forest landscapes in the Iberian Peninsula provides a valuable new tool for environmental researchers, policy makers, and forest management professionals, offering detailed insights that can inform conservation strategies, carbon sequestration efforts, and sustainable forest management practices.</p>}},
  author       = {{Su, Yang and Schwartz, Martin and Fayad, Ibrahim and García, Mariano and Zavala, Miguel A. and Tijerín-Triviño, Julián and Astigarraga, Julen and Cruz-Alonso, Verónica and Liu, Siyu and Zhang, Xianglin and Chen, Songchao and Ritter, François and Besic, Nikola and d'Aspremont, Alexandre and Ciais, Philippe}},
  issn         = {{2052-4463}},
  keywords     = {{Forests; Biomass; Spain; Remote Sensing Technology; Trees; Ecosystem; Climate Change; Deep Learning}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Scientific Data}},
  title        = {{Canopy height and biomass distribution across the forests of Iberian Peninsula}},
  url          = {{http://dx.doi.org/10.1038/s41597-025-05021-9}},
  doi          = {{10.1038/s41597-025-05021-9}},
  volume       = {{12}},
  year         = {{2025}},
}