Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images

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Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment...

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Detalles Bibliográficos
Autores: Wagner F.H., Dalagnol R., Casapia X.T., Streher A.S., Phillips O.L., Gloor E., Aragăo L.E.O.C.
Formato: artículo
Fecha de Publicación:2020
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/2532
Enlace del recurso:https://hdl.handle.net/20.500.12390/2532
https://doi.org/10.3390/rs12142225
Nivel de acceso:acceso abierto
Materia:Very high resolution images
Deep learning
Semantic segmentation
Species distribution
U-net
http://purl.org/pe-repo/ocde/ford#2.07.01
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dc.title.none.fl_str_mv Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
title Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
spellingShingle Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
Wagner F.H.
Very high resolution images
Deep learning
Semantic segmentation
Species distribution
U-net
http://purl.org/pe-repo/ocde/ford#2.07.01
title_short Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
title_full Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
title_fullStr Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
title_full_unstemmed Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
title_sort Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images
author Wagner F.H.
author_facet Wagner F.H.
Dalagnol R.
Casapia X.T.
Streher A.S.
Phillips O.L.
Gloor E.
Aragăo L.E.O.C.
author_role author
author2 Dalagnol R.
Casapia X.T.
Streher A.S.
Phillips O.L.
Gloor E.
Aragăo L.E.O.C.
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Wagner F.H.
Dalagnol R.
Casapia X.T.
Streher A.S.
Phillips O.L.
Gloor E.
Aragăo L.E.O.C.
dc.subject.none.fl_str_mv Very high resolution images
topic Very high resolution images
Deep learning
Semantic segmentation
Species distribution
U-net
http://purl.org/pe-repo/ocde/ford#2.07.01
dc.subject.es_PE.fl_str_mv Deep learning
Semantic segmentation
Species distribution
U-net
dc.subject.ocde.none.fl_str_mv http://purl.org/pe-repo/ocde/ford#2.07.01
description Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. Here, we use a deep learning algorithm called U-net and very high-resolution multispectral images (0.5 m) from GeoEye satellite to identify, segment and map canopy palms over ~3000 km2 of Amazonian forest. The map was used to analyse the spatial distribution of canopy palm trees and its relation to human disturbance and edaphic conditions. The overall accuracy of the map was 95.5% and the F1-score was 0.7. Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pasture areas and lower in forests that have been cut once and abandoned. Additionally, analysis of palm trees' distribution shows that their abundance is controlled naturally by local soil water content, avoiding both flooded and waterlogged areas near rivers and dry areas on the top of the hills. They show two preferential habitats, in the low elevation above the large rivers, and in the slope directly below the hill tops. Overall, their distribution over the region indicates a relatively pristine landscape, albeit within a forest that is critically endangered because of its location between two deforestation fronts and because of illegal cutting. New tree species distribution data, such as the map of all adult canopy palms produced in this work, are urgently needed to support Amazon species inventory and to understand their distribution and diversity. © 2020 by the authors.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/2532
dc.identifier.doi.none.fl_str_mv https://doi.org/10.3390/rs12142225
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85088628575
url https://hdl.handle.net/20.500.12390/2532
https://doi.org/10.3390/rs12142225
identifier_str_mv 2-s2.0-85088628575
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Remote Sensing
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dc.rights.uri.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
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collection CONCYTEC-Institucional
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Canopy palm trees covered 6.4% of the forest canopy and were distributed in more than two million patches that can represent one or more individuals. The density of canopy palms is affected by human disturbance. The post-disturbance density in secondary forests seems to be related to the type of disturbance, being higher in abandoned pasture areas and lower in forests that have been cut once and abandoned. Additionally, analysis of palm trees' distribution shows that their abundance is controlled naturally by local soil water content, avoiding both flooded and waterlogged areas near rivers and dry areas on the top of the hills. They show two preferential habitats, in the low elevation above the large rivers, and in the slope directly below the hill tops. Overall, their distribution over the region indicates a relatively pristine landscape, albeit within a forest that is critically endangered because of its location between two deforestation fronts and because of illegal cutting. New tree species distribution data, such as the map of all adult canopy palms produced in this work, are urgently needed to support Amazon species inventory and to understand their distribution and diversity. © 2020 by the authors.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengMDPI AGRemote Sensinginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/Very high resolution imagesDeep learning-1Semantic segmentation-1Species distribution-1U-net-1http://purl.org/pe-repo/ocde/ford#2.07.01-1Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR imagesinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTECORIGINALRegional Mapping and Spatial Distribution Analysis of Canopy.pdfRegional Mapping and Spatial Distribution Analysis of Canopy.pdfapplication/pdf8531826https://repositorio.concytec.gob.pe/bitstreams/7842c22b-ef22-4010-b8aa-4ec6c36e0824/download58670976bead7c82aa90f0d185b0c76eMD51TEXTRegional Mapping and Spatial Distribution Analysis of Canopy.pdf.txtRegional Mapping and Spatial Distribution Analysis of Canopy.pdf.txtExtracted texttext/plain70350https://repositorio.concytec.gob.pe/bitstreams/25f93afc-facb-4efc-b41e-ad6595346e6b/downloadb72f9478e83d276173d7155ca478aee8MD52THUMBNAILRegional Mapping and Spatial Distribution Analysis of Canopy.pdf.jpgRegional Mapping and Spatial Distribution Analysis of Canopy.pdf.jpgGenerated Thumbnailimage/jpeg5521https://repositorio.concytec.gob.pe/bitstreams/a16617b4-1599-4f4b-9ab1-0d5fd19b503c/downloadb25ef54a57fa3a0fc38c1776b51da029MD5320.500.12390/2532oai:repositorio.concytec.gob.pe:20.500.12390/25322025-01-14 22:00:45.907https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessopen accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="aa1d62ba-0fe2-4fd2-8789-639a052fb62c"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Regional mapping and spatial distribution analysis of Canopy palms in an Amazon forest using deep learning and VHR images</Title> <PublishedIn> <Publication> <Title>Remote Sensing</Title> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <DOI>https://doi.org/10.3390/rs12142225</DOI> <SCP-Number>2-s2.0-85088628575</SCP-Number> <Authors> <Author> <DisplayName>Wagner F.H.</DisplayName> <Person id="rp06514" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Dalagnol R.</DisplayName> <Person id="rp06513" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Casapia X.T.</DisplayName> <Person id="rp06516" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Streher A.S.</DisplayName> <Person id="rp06518" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Phillips O.L.</DisplayName> <Person id="rp06517" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Gloor E.</DisplayName> <Person id="rp06515" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Aragăo L.E.O.C.</DisplayName> <Person id="rp06512" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>MDPI AG</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/licenses/by/4.0/</License> <Keyword>Very high resolution images</Keyword> <Keyword>Deep learning</Keyword> <Keyword>Semantic segmentation</Keyword> <Keyword>Species distribution</Keyword> <Keyword>U-net</Keyword> <Abstract>Mapping plant species at the regional scale to provide information for ecologists and forest managers is a challenge for the remote sensing community. 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