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ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHG...

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Autores principales: Pascarella, Antonio Elia, Giacco, Giovanni, Rigiroli, Mattia, Marrone, Stefano, Sansone, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054486/
https://www.ncbi.nlm.nih.gov/pubmed/36976112
http://dx.doi.org/10.3390/jimaging9030061
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author Pascarella, Antonio Elia
Giacco, Giovanni
Rigiroli, Mattia
Marrone, Stefano
Sansone, Carlo
author_facet Pascarella, Antonio Elia
Giacco, Giovanni
Rigiroli, Mattia
Marrone, Stefano
Sansone, Carlo
author_sort Pascarella, Antonio Elia
collection PubMed
description The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in situ observation becomes essential. To support this critical need, in this work, we introduce ReUse, a simple but effective deep learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The proposed method’s novelty is in using the public above-ground biomass (AGB) data from the European Space Agency’s Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise regressive UNet. The approach has been compared with two literature proposals using a private dataset and human-engineered features. The results show a more remarkable generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Square Error over the runner-up of 16.9 and 14.3 in the area of Vietnam, 4.7 and 5.1 in the area of Myanmar, 8.0 and 1.4 in the area of Central Europe, respectively. As a case study, we also report an analysis made for the Astroni area, a World Wildlife Fund (WWF) natural reserve struck by a large fire, producing predictions consistent with values found by experts in the field after in situ investigations. These results further support the use of such an approach for the early detection of AGB variations in urban and rural areas.
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spelling pubmed-100544862023-03-30 ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation Pascarella, Antonio Elia Giacco, Giovanni Rigiroli, Mattia Marrone, Stefano Sansone, Carlo J Imaging Article The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in situ observation becomes essential. To support this critical need, in this work, we introduce ReUse, a simple but effective deep learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The proposed method’s novelty is in using the public above-ground biomass (AGB) data from the European Space Agency’s Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise regressive UNet. The approach has been compared with two literature proposals using a private dataset and human-engineered features. The results show a more remarkable generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Square Error over the runner-up of 16.9 and 14.3 in the area of Vietnam, 4.7 and 5.1 in the area of Myanmar, 8.0 and 1.4 in the area of Central Europe, respectively. As a case study, we also report an analysis made for the Astroni area, a World Wildlife Fund (WWF) natural reserve struck by a large fire, producing predictions consistent with values found by experts in the field after in situ investigations. These results further support the use of such an approach for the early detection of AGB variations in urban and rural areas. MDPI 2023-03-07 /pmc/articles/PMC10054486/ /pubmed/36976112 http://dx.doi.org/10.3390/jimaging9030061 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pascarella, Antonio Elia
Giacco, Giovanni
Rigiroli, Mattia
Marrone, Stefano
Sansone, Carlo
ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation
title ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation
title_full ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation
title_fullStr ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation
title_full_unstemmed ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation
title_short ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation
title_sort reuse: regressive unet for carbon storage and above-ground biomass estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054486/
https://www.ncbi.nlm.nih.gov/pubmed/36976112
http://dx.doi.org/10.3390/jimaging9030061
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