Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
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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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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1794503497181298688 |