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Registro Completo |
Biblioteca(s): |
Embrapa Clima Temperado; Embrapa Florestas; Embrapa Uva e Vinho. |
Data corrente: |
15/12/2014 |
Data da última atualização: |
12/06/2017 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
HERTER, F. G.; WREGE, M. S.; TONIETTO, J.; FLORES, C. A. |
Afiliação: |
MARCOS SILVEIRA WREGE, CNPF; JORGE TONIETTO, CNPUV; CARLOS ALBERTO FLORES, CPACT. |
Título: |
Adaptação edafoclimática. |
Ano de publicação: |
2014 |
Fonte/Imprenta: |
In: RASEIRA, M. do C. B.; PEREIRA, J. F. M.; CARVALHO, F. L. C. (Ed.). Pessegueiro. Brasília, DF: Embrapa, 2014. p. 45-56. |
Páginas: |
776 p. |
Idioma: |
Português |
Palavras-Chave: |
Zoneamento. |
Thesagro: |
Clima; Dormência; Irrigação; Manejo; Pessego; Pomar; Solo; Topografia. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/120218/1/Pessegueiro-45-56.pdf
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Marc: |
LEADER 00690naa a2200265 a 4500 001 2011202 005 2017-06-12 008 2014 bl uuuu u00u1 u #d 100 1 $aHERTER, F. G. 245 $aAdaptação edafoclimática. 260 $c2014 300 $a776 p. 650 $aClima 650 $aDormência 650 $aIrrigação 650 $aManejo 650 $aPessego 650 $aPomar 650 $aSolo 650 $aTopografia 653 $aZoneamento 700 1 $aWREGE, M. S. 700 1 $aTONIETTO, J. 700 1 $aFLORES, C. A. 773 $tIn: RASEIRA, M. do C. B.; PEREIRA, J. F. M.; CARVALHO, F. L. C. (Ed.). Pessegueiro. Brasília, DF: Embrapa, 2014. p. 45-56.
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Registro original: |
Embrapa Uva e Vinho (CNPUV) |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
05/11/2020 |
Data da última atualização: |
05/11/2020 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
JIANG, X.; LI, G.; LU, D.; MORAN, E.; BATISTELLA, M. |
Afiliação: |
XIANDIE JIANG, Fujian Normal University; GUIYING LI, Fujian Normal University; DENGSHENG LU, Fujian Normal University; EMILIO MORAN, Michigan State University; MATEUS BATISTELLA, CNPTIA. |
Título: |
Modeling forest aboveground carbon density in the Brazilian Amazon with integration of MODIS and Airborne LiDAR data. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Remote Sensing, v. 12, n. 20, p. 1-25, Oct. 2020. |
DOI: |
10.3390/rs12203330 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Aboveground carbon density; Amazônia brasileira; Brazilian Amazon; Densidade de carbono acima do solo; Floresta aleatória; Linear regression; MODIS; Random forest. |
Thesagro: |
Biomassa; Regressão Linear. |
Thesaurus NAL: |
Aboveground biomass; Carbon; Lidar. |
Categoria do assunto: |
-- |
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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