Models of forest variables estimation using multispectral images

Detalhes bibliográficos
Autor(a) principal: Machado, Igor Eloi Silva
Data de Publicação: 2017
Outros Autores: Santos, Micael Moreira, Giongo, Marcos, Carvalho, Edmar Vinicius de, Ganassoli Neto, Eduardo
Tipo de documento: Artigo
Idioma: por
Título da fonte: Pesquisa Florestal Brasileira (Online)
Texto Completo: https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1380
Resumo: Remote sensing techniques for vegetation monitoring has been more used and improved. These techniques are good alternative to be used as as basis for forest inventories. The present study aims to estimate forest variables using multispectral images associated with data from field survey. The studied area was a tropical rain forest of approximately 44,728.5 ha. A forest inventory 100% was carried out providing the volume of tree species with circumference at 1.30 m above soil level (CBH) higher than 110 cm. The used satellite was Landsat 7, scene 227/069. A radiometric correction was performed to obtain the reflectance values. Three plots sizes (40, 80, 120 m radius) were assessed for linear models adjustment. The area spectral behave presented low response on visible region (TM1, TM2 and TM3), increasing on near-infrared (TM4). TM4 band presented higher correlation with CBH (R: -0.5203). The best model to estimate showed a R²aj = 0.387 and Syx = 30.199%, estimating an average volume of 39,61 m³ ha-1. The results showed viability to use satellite images to evaluate dendrometric variables.
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spelling Models of forest variables estimation using multispectral imagesModelos para estimativa de variáveis florestais com a utilização de imagens multiespectraisLandsatInventários florestaisEstimativa de volumeLandsatForest inventoryVolume estimationRemote sensing techniques for vegetation monitoring has been more used and improved. These techniques are good alternative to be used as as basis for forest inventories. The present study aims to estimate forest variables using multispectral images associated with data from field survey. The studied area was a tropical rain forest of approximately 44,728.5 ha. A forest inventory 100% was carried out providing the volume of tree species with circumference at 1.30 m above soil level (CBH) higher than 110 cm. The used satellite was Landsat 7, scene 227/069. A radiometric correction was performed to obtain the reflectance values. Three plots sizes (40, 80, 120 m radius) were assessed for linear models adjustment. The area spectral behave presented low response on visible region (TM1, TM2 and TM3), increasing on near-infrared (TM4). TM4 band presented higher correlation with CBH (R: -0.5203). The best model to estimate showed a R²aj = 0.387 and Syx = 30.199%, estimating an average volume of 39,61 m³ ha-1. The results showed viability to use satellite images to evaluate dendrometric variables.Técnicas de sensoriamento remoto em monitoramento vegetacional vêm sendo cada vez mais utilizadas e melhoradas. A utilização dessas técnicas é uma alternativa potencial para embasar inventários florestais. O presente trabalho tem por objetivo estimar variáveis florestais utilizando imagens multiespectrais associadas a informações obtidas em levantamento de campo. Foi estudada uma área de floresta tropical com aproximadamente 44.728,5 ha. Foi feito um inventário florestal 100%, fornecendo a volumetria das árvores com circunferência a 1,30 m acima do solo (CAP) superior a 110 cm. Foi utilizada a cena 227/069 do Landsat 7, sendo feita correção radiométrica da imagem, obtendo-se os valores de reflectância. Foram avaliados três tamanhos de parcelas circulares (40, 80 e 120 m de raio) para ajuste dos modelos lineares. O comportamento espectral da área apresentou respostas baixas na região do visível (TM1, TM2 e TM3), aumentando no infravermelho próximo (TM4). A banda TM4 apresentou maior correlação com o CAP (R: -0,5203). O melhor modelo para estimativa do volume exibiu R²aj = 0,387 e Syx = 30,199%, estimando um volume médio de 39,61 m³ ha-1. Os resultados demonstraram viabilidade do uso de imagens de satélites para estimativa de variáveis dendrométricas.Embrapa Florestas2017-06-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/138010.4336/2017.pfb.37.90.1380Pesquisa Florestal Brasileira; v. 37 n. 90 (2017): abr./jun.; 171-181Pesquisa Florestal Brasileira; Vol. 37 No. 90 (2017): abr./jun.; 171-1811983-26051809-3647reponame:Pesquisa Florestal Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1380/571Copyright (c) 2017 Igor Eloi Silva Machado, Micael Moreira Santos, Marcos Giongo, Edmar Vinicius de Carvalho, Eduardo Ganassoli Netohttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessMachado, Igor Eloi SilvaSantos, Micael MoreiraGiongo, MarcosCarvalho, Edmar Vinicius deGanassoli Neto, Eduardo2018-01-07T19:01:10Zoai:pfb.cnpf.embrapa.br/pfb:article/1380Revistahttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/PUBhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/oaipfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br1983-26051809-3647opendoar:2018-01-07T19:01:10Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Models of forest variables estimation using multispectral images
Modelos para estimativa de variáveis florestais com a utilização de imagens multiespectrais
title Models of forest variables estimation using multispectral images
spellingShingle Models of forest variables estimation using multispectral images
Machado, Igor Eloi Silva
Landsat
Inventários florestais
Estimativa de volume
Landsat
Forest inventory
Volume estimation
title_short Models of forest variables estimation using multispectral images
title_full Models of forest variables estimation using multispectral images
title_fullStr Models of forest variables estimation using multispectral images
title_full_unstemmed Models of forest variables estimation using multispectral images
title_sort Models of forest variables estimation using multispectral images
author Machado, Igor Eloi Silva
author_facet Machado, Igor Eloi Silva
Santos, Micael Moreira
Giongo, Marcos
Carvalho, Edmar Vinicius de
Ganassoli Neto, Eduardo
author_role author
author2 Santos, Micael Moreira
Giongo, Marcos
Carvalho, Edmar Vinicius de
Ganassoli Neto, Eduardo
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Machado, Igor Eloi Silva
Santos, Micael Moreira
Giongo, Marcos
Carvalho, Edmar Vinicius de
Ganassoli Neto, Eduardo
dc.subject.por.fl_str_mv Landsat
Inventários florestais
Estimativa de volume
Landsat
Forest inventory
Volume estimation
topic Landsat
Inventários florestais
Estimativa de volume
Landsat
Forest inventory
Volume estimation
description Remote sensing techniques for vegetation monitoring has been more used and improved. These techniques are good alternative to be used as as basis for forest inventories. The present study aims to estimate forest variables using multispectral images associated with data from field survey. The studied area was a tropical rain forest of approximately 44,728.5 ha. A forest inventory 100% was carried out providing the volume of tree species with circumference at 1.30 m above soil level (CBH) higher than 110 cm. The used satellite was Landsat 7, scene 227/069. A radiometric correction was performed to obtain the reflectance values. Three plots sizes (40, 80, 120 m radius) were assessed for linear models adjustment. The area spectral behave presented low response on visible region (TM1, TM2 and TM3), increasing on near-infrared (TM4). TM4 band presented higher correlation with CBH (R: -0.5203). The best model to estimate showed a R²aj = 0.387 and Syx = 30.199%, estimating an average volume of 39,61 m³ ha-1. The results showed viability to use satellite images to evaluate dendrometric variables.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-30
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1380
10.4336/2017.pfb.37.90.1380
url https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1380
identifier_str_mv 10.4336/2017.pfb.37.90.1380
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1380/571
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Embrapa Florestas
publisher.none.fl_str_mv Embrapa Florestas
dc.source.none.fl_str_mv Pesquisa Florestal Brasileira; v. 37 n. 90 (2017): abr./jun.; 171-181
Pesquisa Florestal Brasileira; Vol. 37 No. 90 (2017): abr./jun.; 171-181
1983-2605
1809-3647
reponame:Pesquisa Florestal Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Florestal Brasileira (Online)
collection Pesquisa Florestal Brasileira (Online)
repository.name.fl_str_mv Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br
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