Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/20299 |
Resumo: | This study aims to estimate the total biomass aboveground and soil carbon stocks in a Mediterranean riparian forest and identify the contribution of the different species and ecosystem compartments to the overall riparian carbon reservoir. We used a combined field and object-based image analysis (OBIA) approach, based on unmanned aerial vehicle (UAV) multispectral imagery, to assess C stock of three dominant riparian species. A linear discriminator was designed, based on a set of spectral variables previously selected in an optimal way, permitting the classification of the species corresponding to every object in the study area. This made it possible to estimate the area occupied by each species and its contribution to the tree aboveground biomass (AGB). Three uncertainty levels were considered, related to the trade-o between the number of unclassified and misclassified objects, leading to an error control associated with the estimated tree AGB.We found that riparian woodlands dominated by Acacia dealbata Link showed the highest average carbon stock per unit area (251 90 tC ha1) followed by Alnus glutinosa (L.) Gaertner (162 12 tC ha1) and by Salix salviifolia Brot. (73 17 tC ha1), which are mainly related to the stem density, vegetation development and successional stage of the different stands. The woody tree compartment showed the highest inputs (79%), followed by the understory vegetation (12%) and lastly by the soil mineral layer (9%). Spectral vegetation indices developed to suppress saturation effects were consistently selected as important variables for species classification. The total tree AGB in the study area varies from 734 to 1053 tC according to the distinct levels of uncertainty. This study provided the foundations for the assessment of the riparian carbon sequestration and the economic value of the carbon stocks provided by similar Mediterranean riparian forests, a highly relevant ecosystem service for the regulation of climate change effects |
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Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imageryabove ground biomass (AGB)carbon stocksecosystem servicesriparian allometric equationsunmanned aerial vehicle (UAV)This study aims to estimate the total biomass aboveground and soil carbon stocks in a Mediterranean riparian forest and identify the contribution of the different species and ecosystem compartments to the overall riparian carbon reservoir. We used a combined field and object-based image analysis (OBIA) approach, based on unmanned aerial vehicle (UAV) multispectral imagery, to assess C stock of three dominant riparian species. A linear discriminator was designed, based on a set of spectral variables previously selected in an optimal way, permitting the classification of the species corresponding to every object in the study area. This made it possible to estimate the area occupied by each species and its contribution to the tree aboveground biomass (AGB). Three uncertainty levels were considered, related to the trade-o between the number of unclassified and misclassified objects, leading to an error control associated with the estimated tree AGB.We found that riparian woodlands dominated by Acacia dealbata Link showed the highest average carbon stock per unit area (251 90 tC ha1) followed by Alnus glutinosa (L.) Gaertner (162 12 tC ha1) and by Salix salviifolia Brot. (73 17 tC ha1), which are mainly related to the stem density, vegetation development and successional stage of the different stands. The woody tree compartment showed the highest inputs (79%), followed by the understory vegetation (12%) and lastly by the soil mineral layer (9%). Spectral vegetation indices developed to suppress saturation effects were consistently selected as important variables for species classification. The total tree AGB in the study area varies from 734 to 1053 tC according to the distinct levels of uncertainty. This study provided the foundations for the assessment of the riparian carbon sequestration and the economic value of the carbon stocks provided by similar Mediterranean riparian forests, a highly relevant ecosystem service for the regulation of climate change effectsMDPIRepositório da Universidade de LisboaFernandes, Maria RosárioAguiar, Francisca C.Martins, Maria JoãoRico, NunoFerreira, Maria TeresaCorreia, Alexandra C.2020-09-15T11:50:19Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/20299engForests 2020, 11, 37610.3390/f11040376info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-05T01:31:12Zoai:www.repository.utl.pt:10400.5/20299Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:05:04.515736Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
title |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
spellingShingle |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery Fernandes, Maria Rosário above ground biomass (AGB) carbon stocks ecosystem services riparian allometric equations unmanned aerial vehicle (UAV) |
title_short |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
title_full |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
title_fullStr |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
title_full_unstemmed |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
title_sort |
Carbon stock estimation in a Mediterranean riparian forest: a case study combining field data and UAV imagery |
author |
Fernandes, Maria Rosário |
author_facet |
Fernandes, Maria Rosário Aguiar, Francisca C. Martins, Maria João Rico, Nuno Ferreira, Maria Teresa Correia, Alexandra C. |
author_role |
author |
author2 |
Aguiar, Francisca C. Martins, Maria João Rico, Nuno Ferreira, Maria Teresa Correia, Alexandra C. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Fernandes, Maria Rosário Aguiar, Francisca C. Martins, Maria João Rico, Nuno Ferreira, Maria Teresa Correia, Alexandra C. |
dc.subject.por.fl_str_mv |
above ground biomass (AGB) carbon stocks ecosystem services riparian allometric equations unmanned aerial vehicle (UAV) |
topic |
above ground biomass (AGB) carbon stocks ecosystem services riparian allometric equations unmanned aerial vehicle (UAV) |
description |
This study aims to estimate the total biomass aboveground and soil carbon stocks in a Mediterranean riparian forest and identify the contribution of the different species and ecosystem compartments to the overall riparian carbon reservoir. We used a combined field and object-based image analysis (OBIA) approach, based on unmanned aerial vehicle (UAV) multispectral imagery, to assess C stock of three dominant riparian species. A linear discriminator was designed, based on a set of spectral variables previously selected in an optimal way, permitting the classification of the species corresponding to every object in the study area. This made it possible to estimate the area occupied by each species and its contribution to the tree aboveground biomass (AGB). Three uncertainty levels were considered, related to the trade-o between the number of unclassified and misclassified objects, leading to an error control associated with the estimated tree AGB.We found that riparian woodlands dominated by Acacia dealbata Link showed the highest average carbon stock per unit area (251 90 tC ha1) followed by Alnus glutinosa (L.) Gaertner (162 12 tC ha1) and by Salix salviifolia Brot. (73 17 tC ha1), which are mainly related to the stem density, vegetation development and successional stage of the different stands. The woody tree compartment showed the highest inputs (79%), followed by the understory vegetation (12%) and lastly by the soil mineral layer (9%). Spectral vegetation indices developed to suppress saturation effects were consistently selected as important variables for species classification. The total tree AGB in the study area varies from 734 to 1053 tC according to the distinct levels of uncertainty. This study provided the foundations for the assessment of the riparian carbon sequestration and the economic value of the carbon stocks provided by similar Mediterranean riparian forests, a highly relevant ecosystem service for the regulation of climate change effects |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09-15T11:50:19Z 2020 2020-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.5/20299 |
url |
http://hdl.handle.net/10400.5/20299 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Forests 2020, 11, 376 10.3390/f11040376 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799131143171211264 |