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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
05/11/2020 |
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Data da última atualização: |
05/11/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
JIANG, X.; LI, G.; LU, D.; MORAN, E.; BATISTELLA, M. |
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Afiliação: |
XIANDIE JIANG, Fujian Normal University; GUIYING LI, Fujian Normal University; DENGSHENG LU, Fujian Normal University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA. |
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Título: |
Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Remote Sensing, v. 12, n. 20, p. 1-25, Oct. 2020. |
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DOI: |
10.3390/rs12203330 |
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Idioma: |
Inglês |
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Conteúdo: |
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. MenosAbstract: 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 ... Mostrar Tudo |
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Palavras-Chave: |
Aboveground carbon density; Amazônia brasileira; Brazilian Amazon; Densidade de carbono acima do solo; Floresta aleatória; Linear regression; MODIS; Random forest. |
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Thesagro: |
Biomassa; Regressão Linear. |
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Thesaurus Nal: |
Aboveground biomass; Carbon; Lidar. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/217519/1/AP-Modeling-Forest-2020.pdf
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Marc: |
LEADER 02508naa a2200337 a 4500
001 2126323
005 2020-11-05
008 2020 bl uuuu u00u1 u #d
024 7 $a10.3390/rs12203330$2DOI
100 1 $aJIANG, X.
245 $aModeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data.$h[electronic resource]
260 $c2020
520 $aAbstract: 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.
650 $aAboveground biomass
650 $aCarbon
650 $aLidar
650 $aBiomassa
650 $aRegressão Linear
653 $aAboveground carbon density
653 $aAmazônia brasileira
653 $aBrazilian Amazon
653 $aDensidade de carbono acima do solo
653 $aFloresta aleatória
653 $aLinear regression
653 $aMODIS
653 $aRandom forest
700 1 $aLI, G.
700 1 $aLU, D.
700 1 $aMORAN, E.
700 1 $aBATISTELLA, M.
773 $tRemote Sensing$gv. 12, n. 20, p. 1-25, Oct. 2020.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca |
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Registro |
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| Registros recuperados : 10 | |
| 7. |  | LI, G.; LU, D.; MORAN, E.; CALVI, M. F.; DUTRA, L. V.; BATISTELLA, M. Examining deforestation and agropasture dynamics along the Brazilian TransAmazon Highway using multitemporal Landsat imagery. GIScience & Remote Sensing, v. 56, n. 2, p. 161-183, 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 8. | | CAK, A. D.; MORAN, E. F.; FIGUEIREDO, R. de O.; LU, D.; LI, G.; HETRICK, S. Urbanization and small household agricultural land use choices in the Brazilian Amazon and the role for the water chemistry of small streams. Journal of Land Use Science, Abingdon, v. 11, n. 2, p. 203-221, 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Meio Ambiente. |
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| 9. | | AN, F.; LI, G.; LI, Q. X.; LI, K.; CARVALHO, L. J. C. B.; OU, W.; CHEN, S. The comparatively proteomic analysis in response to cold stress in cassava plantlets. Plant Molecular Biology Reporter, v. 34, n. 6, p. 1095-1110, 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 10. | | LU, D.; BATISTELLA, M.; LI, G.; MORAN, E.; HETRICK, S.; FREITAS, C. DA C.; SANT'ANNA, S. J. Land use/cover classification in the Brazilian Amazon using satellite images. Pesquisa Agropecuária Brasileira, Brasilia, DF, v. 47, n. 9, p. 1185-1208, set. 2012. p. 1185-1208.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Territorial; Embrapa Unidades Centrais. |
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| Registros recuperados : 10 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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