La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia

Tree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forest. A proposal for characterizing habitat of forests, obtained from an object-oriented classification of RapidEye multiespectral imagery, based on dissimilar...

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Autores: ALEJANDRA DEL PILAR OCHOA FRANCO, JOSE RENE VALDEZ LAZALDE, HECTOR MANUEL DE LOS SANTOS POSADAS, JOSE LUIS HERNANDEZ STEFANONI, JUAN IGNACIO VALDEZ HERNANDEZ, GREGORIO ANGELES PEREZ
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:México
Institución:Centro de Investigación Científica de Yucatán
Repositorio:Repositorio Institucional CICY
Idioma:español
OAI Identifier:oai:cicy.repositorioinstitucional.mx:1003/1794
Acceso en línea:http://cicy.repositorioinstitucional.mx/jspui/handle/1003/1794
Access Level:acceso abierto
Palabra clave:info:eu-repo/classification/Autores/IMAGE SEGMENTATION
info:eu-repo/classification/Autores/RELATIVE IMPORTANCE VALUE
info:eu-repo/classification/Autores/PERMANOVA
info:eu-repo/classification/Autores/MULTINOMIAL MODEL
info:eu-repo/classification/Autores/TROPICAL FOREST
info:eu-repo/classification/Autores/HABITAT CHARACTERIZATION
info:eu-repo/classification/cti/2
info:eu-repo/classification/cti/24
info:eu-repo/classification/cti/2417
info:eu-repo/classification/cti/241715
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spelling La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifoliaTree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forestALEJANDRA DEL PILAR OCHOA FRANCOJOSE RENE VALDEZ LAZALDEHECTOR MANUEL DE LOS SANTOS POSADASJOSE LUIS HERNANDEZ STEFANONIJUAN IGNACIO VALDEZ HERNANDEZGREGORIO ANGELES PEREZinfo:eu-repo/classification/Autores/IMAGE SEGMENTATIONinfo:eu-repo/classification/Autores/RELATIVE IMPORTANCE VALUEinfo:eu-repo/classification/Autores/PERMANOVAinfo:eu-repo/classification/Autores/MULTINOMIAL MODELinfo:eu-repo/classification/Autores/TROPICAL FORESTinfo:eu-repo/classification/Autores/HABITAT CHARACTERIZATIONinfo:eu-repo/classification/cti/2info:eu-repo/classification/cti/24info:eu-repo/classification/cti/2417info:eu-repo/classification/cti/241715info:eu-repo/classification/cti/241715Tree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forest. A proposal for characterizing habitat of forests, obtained from an object-oriented classification of RapidEye multiespectral imagery, based on dissimilarity matrices of vegetation structure, species diversity and composition is presented. The study area is a forested landscape mosaic after slash and burn agriculture (Ac: 8-23 years ago), selective logging (Fs: 43-53 years ago), and selective logging and forest fire (Fc: 21-28 years ago). The site is located in the central part of Quintana Roo, México, where three vegetation patches were delineated according to remotely sensed multiespectral imagery. Mean differences between vegetation structure properties of each vegetation patch were obtained through a permutational multivariate analysis of variance (P < 0.001). Species richness, stem density per hectare, and the axis-1 scores of the non-metric multidimensional scaling ordination of specific composition were identified as the vegetation attributes more relevant to differentiate the vegetation patches by a multinomial logistic model. Fc vegetation patch is characterized by the greatest mean values on Shannon-Wiener index, species richness, and stem density. The Fs has the greatest mean values of canopy height, basal area, and biomass at 80 percentile, and the Ac vegetation patch has the lowest values of all mentioned metrics. The species with the greatest relative importance value were: Ac: Bursera simaruba and Piscidia piscipula, Fs: Gymnanthes lucida and Manilkara zapota, Fc: G. lucida and B. simaruba. The uncertainty associated with the metrics assessed by vegetation patch was smaller than the uncertainty of the whole area, because of the efficient variability aggregation of the field data. We conclude that multiespectral information is a reliable tool for distinguishing vegetation patches with specific features, as stem density, specific composition, and species richness.2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://cicy.repositorioinstitucional.mx/jspui/handle/1003/1794Revista de Biología Tropical, 67(4), 692-707, 2019reponame:Repositorio Institucional CICYinstname:Centro de Investigación Científica de Yucatáninstacron:CICYspainfo:eu-repo/semantics/datasetDOI/http://dx.doi.org/10.15517/rbt.v67i4.34422 citation:Ochoa-Franco, A. D. P., Valdez-Lazalde, J. R., Santos-Posadas, H. M. D. L., Hernández-Stefanoni, J. L., Valdez-Hernández, J. I., & Ángeles-Pérez, G. (2019). Tree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forest. Revista de Biología Tropical, 67(4), 692-707.info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0oai:cicy.repositorioinstitucional.mx:1003/17942024-08-28T03:18:43Z
dc.title.none.fl_str_mv La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
Tree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forest
title La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
spellingShingle La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
ALEJANDRA DEL PILAR OCHOA FRANCO
info:eu-repo/classification/Autores/IMAGE SEGMENTATION
info:eu-repo/classification/Autores/RELATIVE IMPORTANCE VALUE
info:eu-repo/classification/Autores/PERMANOVA
info:eu-repo/classification/Autores/MULTINOMIAL MODEL
info:eu-repo/classification/Autores/TROPICAL FOREST
info:eu-repo/classification/Autores/HABITAT CHARACTERIZATION
info:eu-repo/classification/cti/2
info:eu-repo/classification/cti/24
info:eu-repo/classification/cti/2417
info:eu-repo/classification/cti/241715
info:eu-repo/classification/cti/241715
title_short La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
title_full La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
title_fullStr La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
title_full_unstemmed La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
title_sort La densidad, riqueza y composición arbóreas definen parches detectados remotamente en una selva subperennifolia
dc.creator.none.fl_str_mv ALEJANDRA DEL PILAR OCHOA FRANCO
JOSE RENE VALDEZ LAZALDE
HECTOR MANUEL DE LOS SANTOS POSADAS
JOSE LUIS HERNANDEZ STEFANONI
JUAN IGNACIO VALDEZ HERNANDEZ
GREGORIO ANGELES PEREZ
author ALEJANDRA DEL PILAR OCHOA FRANCO
author_facet ALEJANDRA DEL PILAR OCHOA FRANCO
JOSE RENE VALDEZ LAZALDE
HECTOR MANUEL DE LOS SANTOS POSADAS
JOSE LUIS HERNANDEZ STEFANONI
JUAN IGNACIO VALDEZ HERNANDEZ
GREGORIO ANGELES PEREZ
author_role author
author2 JOSE RENE VALDEZ LAZALDE
HECTOR MANUEL DE LOS SANTOS POSADAS
JOSE LUIS HERNANDEZ STEFANONI
JUAN IGNACIO VALDEZ HERNANDEZ
GREGORIO ANGELES PEREZ
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv info:eu-repo/classification/Autores/IMAGE SEGMENTATION
info:eu-repo/classification/Autores/RELATIVE IMPORTANCE VALUE
info:eu-repo/classification/Autores/PERMANOVA
info:eu-repo/classification/Autores/MULTINOMIAL MODEL
info:eu-repo/classification/Autores/TROPICAL FOREST
info:eu-repo/classification/Autores/HABITAT CHARACTERIZATION
info:eu-repo/classification/cti/2
info:eu-repo/classification/cti/24
info:eu-repo/classification/cti/2417
info:eu-repo/classification/cti/241715
info:eu-repo/classification/cti/241715
topic info:eu-repo/classification/Autores/IMAGE SEGMENTATION
info:eu-repo/classification/Autores/RELATIVE IMPORTANCE VALUE
info:eu-repo/classification/Autores/PERMANOVA
info:eu-repo/classification/Autores/MULTINOMIAL MODEL
info:eu-repo/classification/Autores/TROPICAL FOREST
info:eu-repo/classification/Autores/HABITAT CHARACTERIZATION
info:eu-repo/classification/cti/2
info:eu-repo/classification/cti/24
info:eu-repo/classification/cti/2417
info:eu-repo/classification/cti/241715
info:eu-repo/classification/cti/241715
description Tree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forest. A proposal for characterizing habitat of forests, obtained from an object-oriented classification of RapidEye multiespectral imagery, based on dissimilarity matrices of vegetation structure, species diversity and composition is presented. The study area is a forested landscape mosaic after slash and burn agriculture (Ac: 8-23 years ago), selective logging (Fs: 43-53 years ago), and selective logging and forest fire (Fc: 21-28 years ago). The site is located in the central part of Quintana Roo, México, where three vegetation patches were delineated according to remotely sensed multiespectral imagery. Mean differences between vegetation structure properties of each vegetation patch were obtained through a permutational multivariate analysis of variance (P < 0.001). Species richness, stem density per hectare, and the axis-1 scores of the non-metric multidimensional scaling ordination of specific composition were identified as the vegetation attributes more relevant to differentiate the vegetation patches by a multinomial logistic model. Fc vegetation patch is characterized by the greatest mean values on Shannon-Wiener index, species richness, and stem density. The Fs has the greatest mean values of canopy height, basal area, and biomass at 80 percentile, and the Ac vegetation patch has the lowest values of all mentioned metrics. The species with the greatest relative importance value were: Ac: Bursera simaruba and Piscidia piscipula, Fs: Gymnanthes lucida and Manilkara zapota, Fc: G. lucida and B. simaruba. The uncertainty associated with the metrics assessed by vegetation patch was smaller than the uncertainty of the whole area, because of the efficient variability aggregation of the field data. We conclude that multiespectral information is a reliable tool for distinguishing vegetation patches with specific features, as stem density, specific composition, and species richness.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://cicy.repositorioinstitucional.mx/jspui/handle/1003/1794
url http://cicy.repositorioinstitucional.mx/jspui/handle/1003/1794
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repo/semantics/datasetDOI/http://dx.doi.org/10.15517/rbt.v67i4.34422 
citation:Ochoa-Franco, A. D. P., Valdez-Lazalde, J. R., Santos-Posadas, H. M. D. L., Hernández-Stefanoni, J. L., Valdez-Hernández, J. I., & Ángeles-Pérez, G. (2019). Tree density, species richness, and composition drive vegetation patches identified from remotely-sensed data in a semi evergreen tropical forest. Revista de Biología Tropical, 67(4), 692-707.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Revista de Biología Tropical, 67(4), 692-707, 2019
reponame:Repositorio Institucional CICY
instname:Centro de Investigación Científica de Yucatán
instacron:CICY
instname_str Centro de Investigación Científica de Yucatán
instacron_str CICY
institution CICY
reponame_str Repositorio Institucional CICY
collection Repositorio Institucional CICY
repository.name.fl_str_mv
repository.mail.fl_str_mv
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