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| Registros recuperados : 8 | |
| 5. |  | LU, D.; CHEN, Q.; WANG, G.; MORAN, E.; BATISTELLA, M.; ZHANG, M.; LAURIN, G. V.; SAAH, D. Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. International Journal of Forestry Research, v. 2012. p. 16, 2012 16 p.| Biblioteca(s): Embrapa Territorial. |
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| 6. | | FENG, Y.; LU, D.; CHEN, Q.; KELLER, M.; MORAN, E.; SANTOS, M. N. dos S.; BOLFE, E. L.; BATISTELLA, M. Examining effective use of data source and modeling algorithms for improving biomass estimation in a moist tropical forest of the brazilian Amazon. International Journal of Digital Earth, London, 2017.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 7. | | CHEN, Q.; LU, D.; KELLER, M.; SANTOS, M. N. DOS; BOLFE, E. L.; FENG, Y.; WANG, C. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. Remote Sensing, v. 8, n. 1, p. 1-17, 2015.| Biblioteca(s): Embrapa Territorial. |
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| 8. | | HYDE, K. D.; ABDEL-WAHAB, M. A.; ABDOLLAHZADEH, J.; ABEYWICKRAMA, P. D.; ABSALAN, S.; AFSHARI, N.; AINSWORTH, A. M.; AKULOV, O. Y.; ALEOSHIN, V. V.; AL-SADI, A. M.; ALVARADO, P.; ALVES, A.; ALVES-SILVA, G.; AMALFI, M.; AMIRA, Y.; AMUHENAGE, T. B.; ANDERSON, J. L.; ANTONÍN, V.; AOUALI, S.; APTROOT, A.; APURILLO, C. C. S.; ARAÚJO, J. P. M.; ARIYAWANSA, H. A.; ARMAND, A.; ARUMUGAM, E.; ASGHARI, R.; ASSIS, D. M. A.; ATIENZA, V.; AVASTHI, S.; AZEVEDO, E.; BAHKALI, A. H.; BAKHSHI, M.; BANIHASHEMI, Z.; BAO, D. F.; BARAL, H. O.; BARATA, M.; BARBOSA, F. R.; BARBOSA, R. N.; BARRETO, R. W.; BASCHIEN, C.; BELAMESIATSEVA, D. B.; BENNETT REUEL, M.; BERA, I.; BEZERRA, J. D. P.; BEZERRA, J. L.; BHAT, D. J.; BHUNJUN, C. S.; BIANCHINOTTI, M. V.; BŁASZKOWSKI, J.; BLONDELLE, A.; BOEKHOUT, T.; BONITO, G.; BOONMEE, S.; BOONYUEN, N.; BREGANT, C.; BUCHANAN, P.; BUNDHUN, D.; BURGAUD, G.; BURGESS, T.; BUYCK, B.; CABARROI-HERNÁNDEZ, M.; CÁCERES, M. E. S.; CAEIRO, M. F.; CAI, L.; CAI, M. F.; CALABON, M. S.; CALAÇA, F. J. S.; CALLALLI, M.; CAMARA, M. P. S.; CANO-LIRA, J. F.; CANTILLO, T.; CAO, B.; CARLAVILLA, J. R.; CARVALHO, A.; CASTAÑEDA-RUIZ, R. F.; CASTLEBURY, L.; CASTRO-JAUREGUI, O.; CATANIA, M. D. V.; CAVALCANTI, L. H.; CAZABONNE, J.; CEDEÑO-SANCHEZ, M. L.; CHAHARMIRI-DOKHAHARANI, S.; CHAIWAN, N.; CHAKRABORTY, N.; CHAVERRI, P.; CHEEWANGKOON, R.; CHEN, C.; CHEN, C. Y.; CHEN, K. H.; CHEN, J.; CHEN, Q.; CHEN, W. H.; CHEN, Y. P.; CHETHANA, K. W. T.; COLEINE, C.; CONDÉ, T. O.; CORAZON-GUIVIN, M. A.; CORTÉS-PÉREZ, A.; COSTA-REZENDE, D. H.; COURTECUISSE, R.; CROUCH, J. A.; CROUS, P. W.; CUI, B. K.; CUI, Y. Y.; SILVA, D. K. A. da; SILVA, G. A. da; SILVA, I. R. da; SILVA, R. M. F. da; SILVA SANTOS, A. C. da; DAI, D. Q.; DAY, Y. C.; DAMM, U.; DARMOSTUK, V.; DAROODI ZOHA; DAS, K.; DAS, K.; DAVOODIAN, N.; DAVYDOV, E. A.; DAYARATHNE, M. C.; DECOCK, C.; DE GROOT, M. D.; DE KESEL, A.; DELA CRUZ, T. E. E.; DE LANGE, R.; DELGADO, G.; DENCHEV, C. M.; DENCHEV, T. T.; OLIVEIRA, N. T. de; SILVA, N. T. de; SOUZA, F. A. de; DENTINGER, B.; DEVADATHA, B.; DIANESE, J. C.; DIMA, B.; DINIZ, A. G.; DISSANAYAKE, A. J.; DISSANAYAKE, L. S.; DOĞAN, H. H.; DOILOM, M.; DOLATABADI, S.; DONG, W.; DONG, Z. Y.; SANTOS, L. A. dos; DRECHSLER-SANTOS, E. R.; DU, T. Y.; DUBEY, M. K.; DUTTA, A. K.; EGIDI, E.; ELLIOTT, T. F.; ELSHAHED, M. S.; ERDOĞDU, M.; ERTZ, D.; ETAYO, J.; EVANS, H. C.; FAN, X. L.; FAN, Y. G.; FEDOSOVA, A. G.; FELL, J.; FERNANDES, I.; FIRMINO, A. L.; FIUZA, P. O.; FLAKUS, A.; SOUZA, C. A. F. de; FRISVAD, J. C.; FRYAR, A. C.; GABALDÓN, T.; GAJANAYAKE, A. J.; GALINDO, L. J.; GANNIBAL, P. B.; GARCIA, D.; GARCÍA-SANDOVAL, S. R.; GARRIDO-BENAVENT, I.; GARZOLI, L.; GAUTAM, A. K.; GE, Z. W.; GENÉ, D. J.; GENTEKAKI, E.; GHOBAD-NEJHAD, M.; GIACHINI, A. J.; GIBERTONI, T. B.; GÓES-NETO, A.; GOMDOLA, D.; FARIAS, A. R. G. de. Global consortium for the classification of fungi and fungus-like taxa. Mycosphere, v. 14, n. 1, p. 1960–2012, 2023.| Biblioteca(s): Embrapa Milho e Sorgo. |
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Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
31/05/2016 |
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Data da última atualização: |
31/05/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
CHEN, Q.; LU, D.; KELLER, M.; SANTOS, M. N. DOS; BOLFE, E. L.; FENG, Y.; WANG, C. |
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Afiliação: |
QI CHEN, Zhejiang A&F University; DENGSHENG LU, Michigan State University; MICHAEL KELLER, USDA Forest Service/ Pesquisador Visitante CNPM; MAIZA NARA DOS SANTOS, BOLSISTA CNPM; EDSON LUIS BOLFE, CNPM; YUNYUN FENG, Zhejiang A&F University; CHANGWEI WANG, University of Hawaii at Manoa. |
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Título: |
Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Remote Sensing, v. 8, n. 1, p. 1-17, 2015. |
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DOI: |
10.3390/rs8010021 |
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Idioma: |
Português |
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Conteúdo: |
Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems. MenosAgroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree ... Mostrar Tudo |
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Palavras-Chave: |
Mixed-effects models. |
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Thesaurus NAL: |
Aboveground biomass; Agroforestry; Allometry; Lidar; Wood density. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/143557/1/4726.pdf
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Marc: |
LEADER 02351naa a2200277 a 4500
001 2045916
005 2016-05-31
008 2015 bl uuuu u00u1 u #d
024 7 $a10.3390/rs8010021$2DOI
100 1 $aCHEN, Q.
245 $aModeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.
260 $c2015
520 $aAgroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.
650 $aAboveground biomass
650 $aAgroforestry
650 $aAllometry
650 $aLidar
650 $aWood density
653 $aMixed-effects models
700 1 $aLU, D.
700 1 $aKELLER, M.
700 1 $aSANTOS, M. N. DOS
700 1 $aBOLFE, E. L.
700 1 $aFENG, Y.
700 1 $aWANG, C.
773 $tRemote Sensing$gv. 8, n. 1, p. 1-17, 2015.
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