Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do INPA |
Texto Completo: | https://repositorio.inpa.gov.br/handle/1/14686 |
Resumo: | Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajo's National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty. © 2016 Bispo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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Bispo, Polyanna da ConceiçãoSantos, João Roberto dosMorisson Valeriano, Márcio deGraça, Paulo Maurício Lima Alencastro deBalzter, HeikoFrança, HelenaBispo, Pitágoras C.2020-04-24T17:00:18Z2020-04-24T17:00:18Z2016https://repositorio.inpa.gov.br/handle/1/1468610.1371/journal.pone.0152009Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajo's National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty. © 2016 Bispo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Volume 11, Número 4Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessAltitudeBrasilCanopyForest StructurePolymorphism, GeneticHeightModelMultiple Linear Regression AnalysisTropical Rain ForestUncertaintyValidation ProcessVegetationBiological ModelRainforestTropic ClimateBrasilModels, BiologicalRainforestTropical ClimatePredictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazoninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePLoS ONEengreponame:Repositório Institucional do INPAinstname:Instituto Nacional de Pesquisas da Amazônia (INPA)instacron:INPAORIGINALartigo-inpa.pdfapplication/pdf3883752https://repositorio.inpa.gov.br/bitstream/1/14686/1/artigo-inpa.pdf16adab3ecba34accc86e3f1369fa5878MD51CC-LICENSElicense_rdfapplication/octet-stream914https://repositorio.inpa.gov.br/bitstream/1/14686/2/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD521/146862020-07-14 10:02:38.769oai:repositorio:1/14686Repositório de PublicaçõesPUBhttps://repositorio.inpa.gov.br/oai/requestopendoar:2020-07-14T14:02:38Repositório Institucional do INPA - Instituto Nacional de Pesquisas da Amazônia (INPA)false |
dc.title.en.fl_str_mv |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
title |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
spellingShingle |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon Bispo, Polyanna da Conceição Altitude Brasil Canopy Forest Structure Polymorphism, Genetic Height Model Multiple Linear Regression Analysis Tropical Rain Forest Uncertainty Validation Process Vegetation Biological Model Rainforest Tropic Climate Brasil Models, Biological Rainforest Tropical Climate |
title_short |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
title_full |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
title_fullStr |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
title_full_unstemmed |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
title_sort |
Predictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazon |
author |
Bispo, Polyanna da Conceição |
author_facet |
Bispo, Polyanna da Conceição Santos, João Roberto dos Morisson Valeriano, Márcio de Graça, Paulo Maurício Lima Alencastro de Balzter, Heiko França, Helena Bispo, Pitágoras C. |
author_role |
author |
author2 |
Santos, João Roberto dos Morisson Valeriano, Márcio de Graça, Paulo Maurício Lima Alencastro de Balzter, Heiko França, Helena Bispo, Pitágoras C. |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Bispo, Polyanna da Conceição Santos, João Roberto dos Morisson Valeriano, Márcio de Graça, Paulo Maurício Lima Alencastro de Balzter, Heiko França, Helena Bispo, Pitágoras C. |
dc.subject.eng.fl_str_mv |
Altitude Brasil Canopy Forest Structure Polymorphism, Genetic Height Model Multiple Linear Regression Analysis Tropical Rain Forest Uncertainty Validation Process Vegetation Biological Model Rainforest Tropic Climate Brasil Models, Biological Rainforest Tropical Climate |
topic |
Altitude Brasil Canopy Forest Structure Polymorphism, Genetic Height Model Multiple Linear Regression Analysis Tropical Rain Forest Uncertainty Validation Process Vegetation Biological Model Rainforest Tropic Climate Brasil Models, Biological Rainforest Tropical Climate |
description |
Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajo's National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty. © 2016 Bispo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
publishDate |
2016 |
dc.date.issued.fl_str_mv |
2016 |
dc.date.accessioned.fl_str_mv |
2020-04-24T17:00:18Z |
dc.date.available.fl_str_mv |
2020-04-24T17:00:18Z |
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 |
https://repositorio.inpa.gov.br/handle/1/14686 |
dc.identifier.doi.none.fl_str_mv |
10.1371/journal.pone.0152009 |
url |
https://repositorio.inpa.gov.br/handle/1/14686 |
identifier_str_mv |
10.1371/journal.pone.0152009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Volume 11, Número 4 |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
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PLoS ONE |
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PLoS ONE |
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