Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest height monitoring by providing repeated observations at hig...
Main Authors: | , , , |
---|---|
Other Authors: | , , , , , , |
Format: | Report |
Language: | English |
Published: |
HAL CCSD
2023
|
Subjects: | |
Online Access: | https://hal.science/hal-04249151 https://hal.science/hal-04249151v2/document https://hal.science/hal-04249151v2/file/TechnicalDocument_FranceForestMap_v20231106.pdf |
id |
ftunivorleans:oai:HAL:hal-04249151v2 |
---|---|
record_format |
openpolar |
spelling |
ftunivorleans:oai:HAL:hal-04249151v2 2023-12-17T10:48:19+01:00 Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France Morin, David Planells, Milena Mermoz, Stéphane Mouret, Florian Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) globeo (globeo) Université d'Orléans (UO) ADEME 2023-09-28 https://hal.science/hal-04249151 https://hal.science/hal-04249151v2/document https://hal.science/hal-04249151v2/file/TechnicalDocument_FranceForestMap_v20231106.pdf en eng HAL CCSD info:eu-repo/semantics/altIdentifier/arxiv/2310.14662 hal-04249151 https://hal.science/hal-04249151 https://hal.science/hal-04249151v2/document https://hal.science/hal-04249151v2/file/TechnicalDocument_FranceForestMap_v20231106.pdf ARXIV: 2310.14662 http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess https://hal.science/hal-04249151 2023 Forest Biomass Sentinel 1 & 2 ALOS-2 PALSAR-2 GEDI Machine learning Forest inventory [SDE]Environmental Sciences [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] info:eu-repo/semantics/preprint Preprints, Working Papers, . 2023 ftunivorleans 2023-11-21T23:43:10Z Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest height monitoring by providing repeated observations at high spatial resolution over large areas. This study uses a machine learning approach that was previously developed to produce local maps of forest parameters (basal area, height, diameter, etc.). The aim of this paper is to present the extension of the approach to much larger scales such as the French national coverage. We used the GEDI Lidar mission as reference height data, and the satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimate forest height and produce a map of France for the year 2020. The height map is then derived into volume and aboveground biomass (AGB) using allometric equations. The validation of the height map with local maps from ALS data shows an accuracy close to the state of the art, with a mean absolute error (MAE) of 4.3 m. Validation on inventory plots representative of French forests shows an MAE of 3.7 m for the height. Estimates are slightly better for coniferous than for broadleaved forests. Volume and AGB maps derived from height shows MAEs of 75 tons/ha and 93 m³/ha respectively. The results aggregated by sylvo-ecoregion and forest types (owner and species) are further improved, with MAEs of 23 tons/ha and 30 m³/ha. The precision of these maps allows to monitor forests locally, as well as helping to analyze forest resources and carbon on a territorial scale or on specific types of forests by combining the maps with geolocated information (administrative area, species, type of owner, protected areas, environmental conditions, etc.). Height, volume and AGB maps produced in this study are made freely available. Report palsa Université d'Orléans: HAL |
institution |
Open Polar |
collection |
Université d'Orléans: HAL |
op_collection_id |
ftunivorleans |
language |
English |
topic |
Forest Biomass Sentinel 1 & 2 ALOS-2 PALSAR-2 GEDI Machine learning Forest inventory [SDE]Environmental Sciences [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
spellingShingle |
Forest Biomass Sentinel 1 & 2 ALOS-2 PALSAR-2 GEDI Machine learning Forest inventory [SDE]Environmental Sciences [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Morin, David Planells, Milena Mermoz, Stéphane Mouret, Florian Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France |
topic_facet |
Forest Biomass Sentinel 1 & 2 ALOS-2 PALSAR-2 GEDI Machine learning Forest inventory [SDE]Environmental Sciences [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] |
description |
Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest height monitoring by providing repeated observations at high spatial resolution over large areas. This study uses a machine learning approach that was previously developed to produce local maps of forest parameters (basal area, height, diameter, etc.). The aim of this paper is to present the extension of the approach to much larger scales such as the French national coverage. We used the GEDI Lidar mission as reference height data, and the satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimate forest height and produce a map of France for the year 2020. The height map is then derived into volume and aboveground biomass (AGB) using allometric equations. The validation of the height map with local maps from ALS data shows an accuracy close to the state of the art, with a mean absolute error (MAE) of 4.3 m. Validation on inventory plots representative of French forests shows an MAE of 3.7 m for the height. Estimates are slightly better for coniferous than for broadleaved forests. Volume and AGB maps derived from height shows MAEs of 75 tons/ha and 93 m³/ha respectively. The results aggregated by sylvo-ecoregion and forest types (owner and species) are further improved, with MAEs of 23 tons/ha and 30 m³/ha. The precision of these maps allows to monitor forests locally, as well as helping to analyze forest resources and carbon on a territorial scale or on specific types of forests by combining the maps with geolocated information (administrative area, species, type of owner, protected areas, environmental conditions, etc.). Height, volume and AGB maps produced in this study are made freely available. |
author2 |
Centre d'études spatiales de la biosphère (CESBIO) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) globeo (globeo) Université d'Orléans (UO) ADEME |
format |
Report |
author |
Morin, David Planells, Milena Mermoz, Stéphane Mouret, Florian |
author_facet |
Morin, David Planells, Milena Mermoz, Stéphane Mouret, Florian |
author_sort |
Morin, David |
title |
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France |
title_short |
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France |
title_full |
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France |
title_fullStr |
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France |
title_full_unstemmed |
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France |
title_sort |
estimation of forest height and biomass from open-access multi-sensor satellite imagery and gedi lidar data: high-resolution maps of metropolitan france |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04249151 https://hal.science/hal-04249151v2/document https://hal.science/hal-04249151v2/file/TechnicalDocument_FranceForestMap_v20231106.pdf |
genre |
palsa |
genre_facet |
palsa |
op_source |
https://hal.science/hal-04249151 2023 |
op_relation |
info:eu-repo/semantics/altIdentifier/arxiv/2310.14662 hal-04249151 https://hal.science/hal-04249151 https://hal.science/hal-04249151v2/document https://hal.science/hal-04249151v2/file/TechnicalDocument_FranceForestMap_v20231106.pdf ARXIV: 2310.14662 |
op_rights |
http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess |
_version_ |
1785572454898335744 |