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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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 | Acesso ao texto completo restrito à biblioteca da Embrapa Unidades Centrais. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
26/05/2017 |
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Data da última atualização: |
26/05/2017 |
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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: |
FENG, Y.; LU, D.; CHEN, Q.; KELLER, M.; MORAN, E.; SANTOS, M. N. dos S.; BOLFE, E. L.; BATISTELLA, M. |
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Afiliação: |
YUNYUN FENG; DENGSHENG LU; QI CHEN; MICHAEL KELLER; EMILIO MORAN; MAIZA NARA DOS SANTOS; EDSON LUIS BOLFE, SIM; MATEUS BATISTELLA, SRI. |
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Título: |
Examining effective use of data source and modeling algorithms for improving biomass estimation in a moist tropical forest of the brazilian Amazon. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
International Journal of Digital Earth, London, 2017. |
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DOI: |
http://dx.doi.org/10.1080/17538947.2017.1301581 |
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Idioma: |
Inglês |
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Conteúdo: |
Previous research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different data sources, vegetation types, and modeling algorithms influence AGB estimation is poorly understood. This research conducts a comparative analysis of different data sources and modeling approaches in improving AGB estimation. RapidEye-based spectral responses and textures, lidar-derived metrics, and their combination were used to develop AGB estimation models. The results indicated that (1) overall, RapidEye data are not suitable for AGB estimation, but when AGB falls within 50?150 Mg/ha, support vector regression based on stratification of vegetation types provided good AGB estimation; (2) Lidar data provided stable and better estimations than RapidEye data; and stratification of vegetation types cannot improve estimation; (3) The combination of lidar and RapidEye data cannot provide better performance than lidar data alone; (4) AGB ranges affect the selection of the best AGB models, and a combination of different estimation results from the best model for each AGB range can improve AGB estimation; (5) This research implies that an optimal procedure for AGB estimation for a specific study exists, depending on the careful selection of data sources, modeling algorithms, forest types, and AGB ranges. |
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Palavras-Chave: |
Algoritmo de modelagem. |
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Thesagro: |
Biomassa; Solo; Vegetação. |
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Thesaurus NAL: |
Aboveground biomass; Lidar; Tropical forests; Vegetation types. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02305naa a2200313 a 4500
001 2070086
005 2017-05-26
008 2017 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.1080/17538947.2017.1301581$2DOI
100 1 $aFENG, Y.
245 $aExamining effective use of data source and modeling algorithms for improving biomass estimation in a moist tropical forest of the brazilian Amazon.$h[electronic resource]
260 $c2017
520 $aPrevious research has explored the potential to integrate lidar and optical data in aboveground biomass (AGB) estimation, but how different data sources, vegetation types, and modeling algorithms influence AGB estimation is poorly understood. This research conducts a comparative analysis of different data sources and modeling approaches in improving AGB estimation. RapidEye-based spectral responses and textures, lidar-derived metrics, and their combination were used to develop AGB estimation models. The results indicated that (1) overall, RapidEye data are not suitable for AGB estimation, but when AGB falls within 50?150 Mg/ha, support vector regression based on stratification of vegetation types provided good AGB estimation; (2) Lidar data provided stable and better estimations than RapidEye data; and stratification of vegetation types cannot improve estimation; (3) The combination of lidar and RapidEye data cannot provide better performance than lidar data alone; (4) AGB ranges affect the selection of the best AGB models, and a combination of different estimation results from the best model for each AGB range can improve AGB estimation; (5) This research implies that an optimal procedure for AGB estimation for a specific study exists, depending on the careful selection of data sources, modeling algorithms, forest types, and AGB ranges.
650 $aAboveground biomass
650 $aLidar
650 $aTropical forests
650 $aVegetation types
650 $aBiomassa
650 $aSolo
650 $aVegetação
653 $aAlgoritmo de modelagem
700 1 $aLU, D.
700 1 $aCHEN, Q.
700 1 $aKELLER, M.
700 1 $aMORAN, E.
700 1 $aSANTOS, M. N. dos S.
700 1 $aBOLFE, E. L.
700 1 $aBATISTELLA, M.
773 $tInternational Journal of Digital Earth, London, 2017.
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