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

<p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p< Ausführliche Beschreibung

Gespeichert in:

Autor*in:	M. Schwartz [verfasserIn] P. Ciais [verfasserIn] A. De Truchis [verfasserIn] J. Chave [verfasserIn] C. Ottlé [verfasserIn] C. Vega [verfasserIn] J.-P. Wigneron [verfasserIn] M. Nicolas [verfasserIn] S. Jouaber [verfasserIn] S. Liu [verfasserIn] M. Brandt [verfasserIn] I. Fayad [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Übergeordnetes Werk:	In: Earth System Science Data - Copernicus Publications, 2009, 15(2023), Seite 4927-4945
Übergeordnetes Werk:	volume:15 ; year:2023 ; pages:4927-4945

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.5194/essd-15-4927-2023

Katalog-ID:	DOAJ098356542

Internformat


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245	1	0	\|a 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
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520			\|a <p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p<
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700	0		\|a I. Fayad \|e verfasserin \|4 aut
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allfields	10.5194/essd-15-4927-2023 doi (DE-627)DOAJ098356542 (DE-599)DOAJ308bd5fb32fe4719adb3224302fe9cfd DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 M. Schwartz verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p< Environmental sciences Geology P. Ciais verfasserin aut A. De Truchis verfasserin aut J. Chave verfasserin aut C. Ottlé verfasserin aut C. Vega verfasserin aut J.-P. Wigneron verfasserin aut M. Nicolas verfasserin aut S. Jouaber verfasserin aut S. Liu verfasserin aut M. Brandt verfasserin aut I. Fayad verfasserin aut I. Fayad verfasserin aut In Earth System Science Data Copernicus Publications, 2009 15(2023), Seite 4927-4945 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:15 year:2023 pages:4927-4945 https://doi.org/10.5194/essd-15-4927-2023 kostenfrei https://doaj.org/article/308bd5fb32fe4719adb3224302fe9cfd kostenfrei https://essd.copernicus.org/articles/15/4927/2023/essd-15-4927-2023.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 4927-4945
spelling	10.5194/essd-15-4927-2023 doi (DE-627)DOAJ098356542 (DE-599)DOAJ308bd5fb32fe4719adb3224302fe9cfd DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 M. Schwartz verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p< Environmental sciences Geology P. Ciais verfasserin aut A. De Truchis verfasserin aut J. Chave verfasserin aut C. Ottlé verfasserin aut C. Vega verfasserin aut J.-P. Wigneron verfasserin aut M. Nicolas verfasserin aut S. Jouaber verfasserin aut S. Liu verfasserin aut M. Brandt verfasserin aut I. Fayad verfasserin aut I. Fayad verfasserin aut In Earth System Science Data Copernicus Publications, 2009 15(2023), Seite 4927-4945 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:15 year:2023 pages:4927-4945 https://doi.org/10.5194/essd-15-4927-2023 kostenfrei https://doaj.org/article/308bd5fb32fe4719adb3224302fe9cfd kostenfrei https://essd.copernicus.org/articles/15/4927/2023/essd-15-4927-2023.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 4927-4945
allfields_unstemmed	10.5194/essd-15-4927-2023 doi (DE-627)DOAJ098356542 (DE-599)DOAJ308bd5fb32fe4719adb3224302fe9cfd DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 M. Schwartz verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p< Environmental sciences Geology P. Ciais verfasserin aut A. De Truchis verfasserin aut J. Chave verfasserin aut C. Ottlé verfasserin aut C. Vega verfasserin aut J.-P. Wigneron verfasserin aut M. Nicolas verfasserin aut S. Jouaber verfasserin aut S. Liu verfasserin aut M. Brandt verfasserin aut I. Fayad verfasserin aut I. Fayad verfasserin aut In Earth System Science Data Copernicus Publications, 2009 15(2023), Seite 4927-4945 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:15 year:2023 pages:4927-4945 https://doi.org/10.5194/essd-15-4927-2023 kostenfrei https://doaj.org/article/308bd5fb32fe4719adb3224302fe9cfd kostenfrei https://essd.copernicus.org/articles/15/4927/2023/essd-15-4927-2023.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 4927-4945
allfieldsGer	10.5194/essd-15-4927-2023 doi (DE-627)DOAJ098356542 (DE-599)DOAJ308bd5fb32fe4719adb3224302fe9cfd DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 M. Schwartz verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p< Environmental sciences Geology P. Ciais verfasserin aut A. De Truchis verfasserin aut J. Chave verfasserin aut C. Ottlé verfasserin aut C. Vega verfasserin aut J.-P. Wigneron verfasserin aut M. Nicolas verfasserin aut S. Jouaber verfasserin aut S. Liu verfasserin aut M. Brandt verfasserin aut I. Fayad verfasserin aut I. Fayad verfasserin aut In Earth System Science Data Copernicus Publications, 2009 15(2023), Seite 4927-4945 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:15 year:2023 pages:4927-4945 https://doi.org/10.5194/essd-15-4927-2023 kostenfrei https://doaj.org/article/308bd5fb32fe4719adb3224302fe9cfd kostenfrei https://essd.copernicus.org/articles/15/4927/2023/essd-15-4927-2023.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 4927-4945
allfieldsSound	10.5194/essd-15-4927-2023 doi (DE-627)DOAJ098356542 (DE-599)DOAJ308bd5fb32fe4719adb3224302fe9cfd DE-627 ger DE-627 rakwb eng GE1-350 QE1-996.5 M. Schwartz verfasserin aut 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 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p< Environmental sciences Geology P. Ciais verfasserin aut A. De Truchis verfasserin aut J. Chave verfasserin aut C. Ottlé verfasserin aut C. Vega verfasserin aut J.-P. Wigneron verfasserin aut M. Nicolas verfasserin aut S. Jouaber verfasserin aut S. Liu verfasserin aut M. Brandt verfasserin aut I. Fayad verfasserin aut I. Fayad verfasserin aut In Earth System Science Data Copernicus Publications, 2009 15(2023), Seite 4927-4945 (DE-627)590283227 (DE-600)2475469-9 18663516 nnns volume:15 year:2023 pages:4927-4945 https://doi.org/10.5194/essd-15-4927-2023 kostenfrei https://doaj.org/article/308bd5fb32fe4719adb3224302fe9cfd kostenfrei https://essd.copernicus.org/articles/15/4927/2023/essd-15-4927-2023.pdf kostenfrei https://doaj.org/toc/1866-3508 Journal toc kostenfrei https://doaj.org/toc/1866-3516 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 4927-4945
language	English
source	In Earth System Science Data 15(2023), Seite 4927-4945 volume:15 year:2023 pages:4927-4945
sourceStr	In Earth System Science Data 15(2023), Seite 4927-4945 volume:15 year:2023 pages:4927-4945
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Environmental sciences Geology
isfreeaccess_bool	true
container_title	Earth System Science Data
authorswithroles_txt_mv	M. Schwartz @@aut@@ P. Ciais @@aut@@ A. De Truchis @@aut@@ J. Chave @@aut@@ C. Ottlé @@aut@@ C. Vega @@aut@@ J.-P. Wigneron @@aut@@ M. Nicolas @@aut@@ S. Jouaber @@aut@@ S. Liu @@aut@@ M. Brandt @@aut@@ I. Fayad @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
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Schwartz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a"><p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. 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callnumber-first	G - Geography, Anthropology, Recreation
author	M. Schwartz
spellingShingle	M. Schwartz misc GE1-350 misc QE1-996.5 misc Environmental sciences misc Geology 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
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topic_title	GE1-350 QE1-996.5 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
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title	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
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title_full	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
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title_sort	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
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title_auth	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
abstract	<p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p<
abstractGer	<p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p<
abstract_unstemmed	<p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p<
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title_short	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
url	https://doi.org/10.5194/essd-15-4927-2023 https://doaj.org/article/308bd5fb32fe4719adb3224302fe9cfd https://essd.copernicus.org/articles/15/4927/2023/essd-15-4927-2023.pdf https://doaj.org/toc/1866-3508 https://doaj.org/toc/1866-3516
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Schwartz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a"><p<The contribution of forests to carbon storage and biodiversity conservation highlights the need for accurate forest height and biomass mapping and monitoring. In France, forests are managed mainly by private owners and divided into small stands, requiring 10 to 50 m spatial resolution data to be correctly separated. Further, 35 % of the French forest territory is covered by mountains and Mediterranean forests which are managed very extensively. In this work, we used a deep-learning model based on multi-stream remote-sensing measurements (NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission and ESA's Copernicus Sentinel-1 and Sentinel-2 satellites) to create a 10 m resolution canopy height map of France for 2020 (FORMS-H). In a second step, with allometric equations fitted to the French National Forest Inventory (NFI) plot data, we created a 30 m resolution above-ground biomass density (AGBD) map (Mg ha<span class="inline-formula"<<sup<−1</sup<</span<) of France (FORMS-B). Extensive validation was conducted. First, independent datasets from airborne laser scanning (ALS) and NFI data from thousands of plots reveal a mean absolute error (MAE) of 2.94 m for FORMS-H, which outperforms existing canopy height models. Second, FORMS-B was validated using two independent forest inventory datasets from the Renecofor permanent forest plot network and from the GLORIE forest inventory with MAE of 59.6 and 19.6 Mg ha<span class="inline-formula"<<sup<−1</sup<</span<, respectively, providing greater performance than other AGBD products sampled over France. Finally, we compared FORMS-V (for volume) with wood volume estimations at the ecological region scale and obtained an <span class="inline-formula"<<i<R</i<<sup<2</sup<</span< of 0.63 with an MAE of 30 m<span class="inline-formula"<<sup<3</sup<</span< ha<span class="inline-formula"<<sup<−1</sup<</span<. These results highlight the importance of coupling remote-sensing technologies with recent advances in computer science to bring material insights to climate-efficient forest management policies. Additionally, our approach is based on open-access data having global coverage and a high spatial and temporal resolution, making the maps reproducible and easily scalable. FORMS products can be accessed from <a href="https://doi.org/10.5281/zenodo.7840108"<https://doi.org/10.5281/zenodo.7840108</a< (Schwartz et al., 2023).</p<</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Environmental sciences</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Geology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">P. Ciais</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">A. 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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

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