Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-337-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-337-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
22 Jan 2025
Methods for assessment of models |
| 22 Jan 2025
| 22 Jan 2025
Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?
Nikola Besic
CORRESPONDING AUTHOR
IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France
Nicolas Picard
Groupement d'Intérêt Public (GIP) Ecofor, 75116 Paris, France
Cédric Vega
IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France
Jean-Daniel Bontemps
IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France
Lionel Hertzog
IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France
Jean-Pierre Renaud
IGN, ENSG, Laboratoire d'inventaire forestier (LIF), 54000 Nancy, France
Office National des Forêts RDI, 54600 Villers-lès-Nancy, France
Fajwel Fogel
Department of Computer Science, École Normale Supérieure, 75230 Paris, France
Martin Schwartz
LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Agnès Pellissier-Tanon
LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Gabriel Destouet
UMR SILVA, INRAE, AgroParisTech, Université de Lorraine, 54280 Champenoux, France
Frédéric Mortier
CIRAD, Forêts et Sociétés, 34398 Montpellier, France
Forêts et Sociétés, University of Montpellier, CIRAD, 34090 Montpellier, France
Milena Planells-Rodriguez
CESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, 31401 Toulouse, France
Philippe Ciais
LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, 91191 Gif-sur-Yvette, France
Viewed
Total article views: 5,918 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Jun 2024)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,515 | 820 | 583 | 5,918 | 248 | 358 |
- HTML: 4,515
- PDF: 820
- XML: 583
- Total: 5,918
- BibTeX: 248
- EndNote: 358
Total article views: 4,440 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Jan 2025)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,760 | 473 | 207 | 4,440 | 215 | 270 |
- HTML: 3,760
- PDF: 473
- XML: 207
- Total: 4,440
- BibTeX: 215
- EndNote: 270
Total article views: 1,478 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Jun 2024)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 755 | 347 | 376 | 1,478 | 33 | 88 |
- HTML: 755
- PDF: 347
- XML: 376
- Total: 1,478
- BibTeX: 33
- EndNote: 88
Viewed (geographical distribution)
Total article views: 5,918 (including HTML, PDF, and XML)
Thereof 5,816 with geography defined
and 102 with unknown origin.
Total article views: 4,440 (including HTML, PDF, and XML)
Thereof 4,342 with geography defined
and 98 with unknown origin.
Total article views: 1,478 (including HTML, PDF, and XML)
Thereof 1,474 with geography defined
and 4 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
16 citations as recorded by crossref.
- An improved forest canopy height mapping method by combining GAN-enhanced optical imagery, SAR, and GEDI data R. Wang et al. https://doi.org/10.1016/j.tfp.2025.101131
- A 30 m forest dominant height dataset for China in 2020 Y. Chen et al. https://doi.org/10.1038/s41597-026-07546-z
- Retrieving yearly forest growth from satellite data: A deep learning based approach M. Schwartz et al. https://doi.org/10.1016/j.rse.2025.114959
- Annual canopy height mapping in Italy from Landsat and multi-source LiDAR for disturbance and recovery monitoring Y. Su et al. https://doi.org/10.1016/j.ecoinf.2026.104036
- me4soc: a multi-model ensemble interface for soil organic carbon predictions E. Bruni et al. https://doi.org/10.1016/j.ecolmodel.2026.111716
- Determining vertical structure of forests in Poland using a semi-automated approach based on ALS data P. Janiec et al. https://doi.org/10.1016/j.ecolind.2025.113825
- Straightforward model-based approach using only field data and open-source maps to improve carbon stock estimates for REDD + projects L. Haneda et al. https://doi.org/10.1038/s41598-026-37201-x
- Inferencia asistida por modelos para la estimación media de volumen y biomasa forestal en México E. Velasco Bautista et al. https://doi.org/10.29298/rmcf.v17i96.1646
- Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model B. Hu et al. https://doi.org/10.1016/j.geoderma.2025.117446
- Evaluating the reliability of the 1-meter resolution meta-estimated global canopy height map for estimating forest structural metrics in Alpine environment M. Torresani et al. https://doi.org/10.1016/j.ecoinf.2026.103950
- Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France H. Cuny et al. https://doi.org/10.5194/bg-23-2365-2026
- Synergistic use of ICESat-2 lidar data and Sentinel-2 imagery for assessing hurricane-driven forest changes A. Gautam et al. https://doi.org/10.1007/s10661-025-14749-1
- A modelling framework to estimate canopy height at local scale using optical and radar images paired with GEDI measurements in Mediterranean-like landscapes J. Pereira-Pires et al. https://doi.org/10.1080/17538947.2026.2687201
- Super-resolved canopy height mapping from Sentinel-2 time series using airborne LiDAR HD reference data across metropolitan France E. Kalinicheva et al. https://doi.org/10.1016/j.rse.2026.115536
- Multitemporal UAV-Based Estimation of Kenaf (Hibiscus cannabinus L.) Plant Height Under Nitrogen and Compost Treatments T. Yoon et al. https://doi.org/10.3390/agronomy16181805
- Digital Twins in Forest Management Using Scalable Deep Learning Pipeline M. Keskes & M. Niţă https://doi.org/10.1109/JSTARS.2026.3666924
16 citations as recorded by crossref.
- An improved forest canopy height mapping method by combining GAN-enhanced optical imagery, SAR, and GEDI data R. Wang et al. https://doi.org/10.1016/j.tfp.2025.101131
- A 30 m forest dominant height dataset for China in 2020 Y. Chen et al. https://doi.org/10.1038/s41597-026-07546-z
- Retrieving yearly forest growth from satellite data: A deep learning based approach M. Schwartz et al. https://doi.org/10.1016/j.rse.2025.114959
- Annual canopy height mapping in Italy from Landsat and multi-source LiDAR for disturbance and recovery monitoring Y. Su et al. https://doi.org/10.1016/j.ecoinf.2026.104036
- me4soc: a multi-model ensemble interface for soil organic carbon predictions E. Bruni et al. https://doi.org/10.1016/j.ecolmodel.2026.111716
- Determining vertical structure of forests in Poland using a semi-automated approach based on ALS data P. Janiec et al. https://doi.org/10.1016/j.ecolind.2025.113825
- Straightforward model-based approach using only field data and open-source maps to improve carbon stock estimates for REDD + projects L. Haneda et al. https://doi.org/10.1038/s41598-026-37201-x
- Inferencia asistida por modelos para la estimación media de volumen y biomasa forestal en México E. Velasco Bautista et al. https://doi.org/10.29298/rmcf.v17i96.1646
- Mapping and understanding the regional farmland SOC distribution in southern China using a Bayesian spatial model B. Hu et al. https://doi.org/10.1016/j.geoderma.2025.117446
- Evaluating the reliability of the 1-meter resolution meta-estimated global canopy height map for estimating forest structural metrics in Alpine environment M. Torresani et al. https://doi.org/10.1016/j.ecoinf.2026.103950
- Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France H. Cuny et al. https://doi.org/10.5194/bg-23-2365-2026
- Synergistic use of ICESat-2 lidar data and Sentinel-2 imagery for assessing hurricane-driven forest changes A. Gautam et al. https://doi.org/10.1007/s10661-025-14749-1
- A modelling framework to estimate canopy height at local scale using optical and radar images paired with GEDI measurements in Mediterranean-like landscapes J. Pereira-Pires et al. https://doi.org/10.1080/17538947.2026.2687201
- Super-resolved canopy height mapping from Sentinel-2 time series using airborne LiDAR HD reference data across metropolitan France E. Kalinicheva et al. https://doi.org/10.1016/j.rse.2026.115536
- Multitemporal UAV-Based Estimation of Kenaf (Hibiscus cannabinus L.) Plant Height Under Nitrogen and Compost Treatments T. Yoon et al. https://doi.org/10.3390/agronomy16181805
- Digital Twins in Forest Management Using Scalable Deep Learning Pipeline M. Keskes & M. Niţă https://doi.org/10.1109/JSTARS.2026.3666924
Saved (final revised paper)
Latest update: 26 Oct 2026
Short summary
The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive analysis of these remote-sensing-based products.
The creation of advanced mapping models for forest attributes, utilizing remote sensing data and...
