Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.

Detalhes bibliográficos
Autor(a) principal: JIANG, X.
Data de Publicação: 2020
Outros Autores: LI, G., LU, D., MORAN, E., BATISTELLA, M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126323
Resumo: Abstract: Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coecient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it dicult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.
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spelling Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.Densidade de carbono acima do soloFloresta aleatóriaAmazônia brasileiraRandom forestMODISBrazilian AmazonLinear regressionAboveground carbon densityRegressão LinearBiomassaAboveground biomassCarbonLidarAbstract: Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coecient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it dicult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.XIANDIE JIANG, Fujian Normal University; GUIYING LI, Fujian Normal University; DENGSHENG LU, Fujian Normal University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA.JIANG, X.LI, G.LU, D.MORAN, E.BATISTELLA, M.2020-11-06T09:15:33Z2020-11-06T09:15:33Z2020-11-052020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleRemote Sensing, v. 12, n. 20, p. 1-25, Oct. 2020.http://www.alice.cnptia.embrapa.br/alice/handle/doc/112632310.3390/rs12203330enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2020-11-06T09:15:42Zoai:www.alice.cnptia.embrapa.br:doc/1126323Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542020-11-06T09:15:42falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542020-11-06T09:15:42Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
title Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
spellingShingle Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
JIANG, X.
Densidade de carbono acima do solo
Floresta aleatória
Amazônia brasileira
Random forest
MODIS
Brazilian Amazon
Linear regression
Aboveground carbon density
Regressão Linear
Biomassa
Aboveground biomass
Carbon
Lidar
title_short Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
title_full Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
title_fullStr Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
title_full_unstemmed Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
title_sort Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
author JIANG, X.
author_facet JIANG, X.
LI, G.
LU, D.
MORAN, E.
BATISTELLA, M.
author_role author
author2 LI, G.
LU, D.
MORAN, E.
BATISTELLA, M.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv XIANDIE JIANG, Fujian Normal University; GUIYING LI, Fujian Normal University; DENGSHENG LU, Fujian Normal University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA.
dc.contributor.author.fl_str_mv JIANG, X.
LI, G.
LU, D.
MORAN, E.
BATISTELLA, M.
dc.subject.por.fl_str_mv Densidade de carbono acima do solo
Floresta aleatória
Amazônia brasileira
Random forest
MODIS
Brazilian Amazon
Linear regression
Aboveground carbon density
Regressão Linear
Biomassa
Aboveground biomass
Carbon
Lidar
topic Densidade de carbono acima do solo
Floresta aleatória
Amazônia brasileira
Random forest
MODIS
Brazilian Amazon
Linear regression
Aboveground carbon density
Regressão Linear
Biomassa
Aboveground biomass
Carbon
Lidar
description Abstract: Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. The ACD estimation model, which was developed by Longo et al. in the same study area, was used to map ACD distribution in the 23 sites. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. The evaluation of modeling results indicated that ACD can be successfully estimated with a coecient of determination of 0.67 and root mean square error of 4.18 kg C/m2 using RF based on spectral indices. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. These uncertainties in ACD estimation using MODIS data make it dicult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.
publishDate 2020
dc.date.none.fl_str_mv 2020-11-06T09:15:33Z
2020-11-06T09:15:33Z
2020-11-05
2020
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Remote Sensing, v. 12, n. 20, p. 1-25, Oct. 2020.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126323
10.3390/rs12203330
identifier_str_mv Remote Sensing, v. 12, n. 20, p. 1-25, Oct. 2020.
10.3390/rs12203330
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1126323
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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