Articles | Volume 15, issue 11
Data description paper
02 Nov 2023
Data description paper |  | 02 Nov 2023

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach

Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-196', Anonymous Referee #1, 10 Jul 2023
    • AC1: 'Reply on RC1', Martin Schwartz, 06 Sep 2023
  • RC2: 'Comment on essd-2023-196', Anonymous Referee #2, 12 Aug 2023
    • AC2: 'Reply on RC2', Martin Schwartz, 06 Sep 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Martin Schwartz on behalf of the Authors (07 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 Sep 2023) by Jia Yang
AR by Martin Schwartz on behalf of the Authors (20 Sep 2023)  Manuscript 
Short summary
As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote-sensing sensors, and artificial intelligence methods, we created high-resolution tree height, wood volume, and biomass maps of metropolitan France that outperform previous products. This study, based on freely available data, provides essential information to support climate-efficient forest management policies at a low cost.
Final-revised paper