Please use this identifier to cite or link to this item: https://rima.ufrrj.br/jspui/handle/20.500.14407/11299
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVilanova, Regiane Souza
dc.date.accessioned2023-12-22T01:49:51Z-
dc.date.available2023-12-22T01:49:51Z-
dc.date.issued2021-08-18
dc.identifier.citationVILANOVA, Regiane Souza. Entendendo os mecanismos da floresta amazônica para manutenção da sua produtividade diante de mudanças antrópicas e climáticas. 2021. 81 f. Dissertação (Mestrado em Ciências Ambientais e Florestais) - Instituto de Floresta, Universidade Federal Rural do Rio de Janeiro, Seropédica.por
dc.identifier.urihttps://rima.ufrrj.br/jspui/handle/20.500.14407/11299-
dc.description.abstractAs florestas tropicais amazônicas estão entre as áreas mais importantes e de maior biodiversidade da terra. Elas contribuem para as funções do ecossistema, incluindo a regulação do fluxo da água e a manutenção de um dos mais importantes sumidouros de carbono do planeta, e fornecem recursos para atividades econômicas importantes, como madeira e produtos não madeireiros, peixes e outros alimentos. No entanto, o desmatamento tropical-equatorial causado pela expansão das atividades agrícolas e exploração madeireira insustentável tem causado perdas e degradação dessa floresta. A falta de fiscalização e políticas voltadas para a preservação só fortalece esse cenário devastador. Nas últimas décadas, pesquisas têm sido realizadas para quantificar essas perdas e entender melhor os diversos agentes atuantes sobre as florestas Amazônicas. Estudo sobre a evapotranspiração, eventos climáticos extremos, incêndios, carbono e absorção de luz pela planta tem ajudado no entendimento desses fatores, bem como na elaboração de estratégias de mitigação dos impactos. Este estudo investiga os diversos mecanismos utilizados pelas florestas para manter-se em pé e produtiva, diante das intervenções humanas e alterações climáticas severas, combinando, analisando e fazendo projeções futuras de diferentes tipos de agentes atuantes na região Amazônica. A tese foi dividida em três capítulos, onde o primeiro avaliou o estado de saúde da vegetação para o estado do Amazonas, também se verificou correlações positivas e negativas desse índice com outras variáveis como, temperatura do ar, chuvas, umidade do solo, focos de incêndio e temperatura da superfície terrestre. Ainda neste capítulo, foi aplicado o Autoregressive Integrated Moving Average (ARIMA) na série do Vegetation Health Index (VHI) para simulação futura da saúde da vegetação, o que nos permitiu verificar como será o comportamento da floresta no futuro, ano de 2030. No segundo capítulo, foram utilizados dados de sensoriamento remoto, as seis tipologias florestais encontradas para o estado e elementos meteorológicos de quatorze estações meteorológicas convencionais distribuídas em todo o estado do Amazonas, para investigar os processos de degradação da vegetação em ano de eventos do fenômeno El Niño Oscilação-Sul. O terceiro capítulo, foi focado na melhoria dos dados proveniente de torre de fluxo, que são usados para fazer inferências sobre a vegetação. Aplicou-se a metodologia ARIMA no preenchimento das falhas de dados de produtividade primária bruta proveniente de uma torre de fluxo (K34) no estado do amazonas.por
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpor
dc.formatapplication/pdf*
dc.languageporpor
dc.publisherUniversidade Federal Rural do Rio de Janeiropor
dc.rightsAcesso Abertopor
dc.subjectModelagem futurapor
dc.subjectsaúde da vegetaçãopor
dc.subjectdegradação ambientalpor
dc.subjectFuture modelingeng
dc.subjectvegetation healtheng
dc.subjectenvironmental degradationeng
dc.titleEntendendo os mecanismos da floresta amazônica para manutenção da sua produtividade diante de mudanças antrópicas e climáticaspor
dc.title.alternativeUnderstanding the mechanisms of the Amazon forest to maintain its productivity in the face of anthropic and climate changeseng
dc.typeDissertaçãopor
dc.description.abstractOtherAmazon rainforests are among the most important and most biodiverse areas on earth. They contribute to ecosystem functions, including regulating water flow and maintaining one of the planet's most important carbon sinks, and provide resources for important economic activities such as timber and non-timber products, fish and other foods. However, tropical deforestation caused by the expansion of agricultural activities and unsustainable logging has caused losses and degradation of this forest However, tropical-equatorial deforestation caused by the expansion of agricultural activities and unsustainable logging has caused losses and degradation of this forest. The lack of inspection and conservation-oriented policies only strengthens this devastating scenario. A study on evapotranspiration, extreme weather events, fires, carbon, and light absorption by the plant has helped in understanding these factors, as well as in the development of impact mitigation strategies. This study investigates the various mechanisms used by forests to remain standing and productive, in the face of human interventions and severe climate change, combining, analyzing and making future projections of different types of agents operating in the Amazon region. The thesis was divided into three chapters, where the first one assessed the health status of the vegetation for the state of Amazonas, there were also positive and negative correlations of this index with other variables such as air temperature, rainfall, soil moisture, foci of fire and surface temperature. Also in this chapter, the Autoregressive Integrated Moving Average (ARIMA) was applied in the Vegetation Health Index (VHI) series for future simulation of vegetation health, which allowed us to verify how the forest will behave in the future, year 2030 In the second chapter, remote sensing data were used, the six forest typologies found for the state and meteorological elements of fourteen conventional weather stations distributed throughout the state of Amazonas, to investigate the processes of vegetation degradation in the year of events of the El Niño Oscilação-Sul phenomenon. The third chapter was focused on improving the data coming from the flow tower, which are used to make inferences about the vegetation. The ARIMA methodology was applied in filling the failures of raw primary productivity data from a flow tower (K34) in the state of Amazonas.eng
dc.contributor.advisor1Delgado, Rafael Coll
dc.contributor.advisor1ID001.729.560-21por
dc.contributor.advisor1IDhttps://orcid.org/0000-0002-3157-2277por
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/1178948690201659por
dc.contributor.advisor-co1Teodoro, Paulo Eduardo
dc.contributor.advisor-co1ID038.790.881-10por
dc.contributor.advisor-co1IDhttps://orcid.org/0000-0002-8236-542Xpor
dc.contributor.advisor-co1Latteshttp://lattes.cnpq.br/3731198010625970por
dc.contributor.referee1Delgado, Rafael Coll
dc.contributor.referee1ID001.729.560-21por
dc.contributor.referee1IDhttps://orcid.org/0000-0002-3157-2277por
dc.contributor.referee1Latteshttp://lattes.cnpq.br/1178948690201659por
dc.contributor.referee2Silva Junior, Carlos Antonio da
dc.contributor.referee2IDhttps://orcid.org/0000-0002-7102-2077por
dc.contributor.referee2Latteshttp://lattes.cnpq.br/4249094513528309por
dc.contributor.referee3Pereira, Marcos Gervasio
dc.contributor.referee3IDhttps://orcid.org/0000-0002-1402-3612por
dc.contributor.referee3Latteshttp://lattes.cnpq.br/3657759682534978por
dc.contributor.referee4Wanderley, Henderson Silva
dc.contributor.referee4IDhttps://orcid.org/0000-0002-4031-3509por
dc.contributor.referee4Latteshttp://lattes.cnpq.br/9838743472295687por
dc.creator.ID978.036.772-15por
dc.creator.Latteshttp://lattes.cnpq.br/4224541019594112por
dc.publisher.countryBrasilpor
dc.publisher.departmentInstituto de Florestaspor
dc.publisher.initialsUFRRJpor
dc.publisher.programPrograma de Pós-Graduação em Ciências Ambientais e Florestaispor
dc.relation.referencesAFRIFA-YAMOAH, E.; MUELLER, U. A.; TAYLOR, S. M.; FISHER, A. J. Missing data imputation of high-resolution temporalclimate time series data. Meteorol Applications, v. 2, n. 1, p. e1873, 2020. https://doi.org/10.1002/met.1873. AHLSTRÖM, A.; RAUPACH, M. R.; SCHURGERS, G.; SMITH, B.; ARNETH, A.; JUNG, M.; REICHSTEIN, M.; CANADELL, J. G.; FRIEDLINGSTEIN, P.; JAIN, A. K.; KATO, E.; POULTER, B.; SITCH, S.; STOCKER, B. D.; VIOVY, N.; WANG, Y. P.; WILTSHIRE, A.; ZAEHLE, S.; ZENG, N. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science, v. 348, n. 6237, p. 895–899. 2015. https://doi.org/10.1126/science.aaa1668. ALVARES C. A.; STAPE J. L.; SENTELHAS P. C.; GONÇALVES J. L. M.; SPAROVEK G. Koppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711–728, 2013. doi: 10.1127/0941-2948/2013/0507. ARAÚJO, A. C.; NOBRE, A. D.; KRUIJT, B.; ELBERS, J. A.; DALLAROSA, R.; STEFANI, P.; VON RANDOW, C.; MANZI, A. O.; CULF, A. D.; GASH, J. H. C.; VALENTINI, R.; KABAT, P. Comparative measurements of carbon dioxide fluxes from two nearby towers in a central Amazonian rainforest: The Manaus LBA site. Journal of Geophysical Research – Atmospheres, v. 107, n. 20, p. 8090, 2002. https://doi.org/10.1029/2001JD000676. BALDOCCHI, D.; PENUELAS, J. The physics and ecology of mining carbon dioxide from the atmosphere by ecosystems. Global Change Biology, v. 25, n. 4, p. 1191-1197, 2019. https://doi.org/10.1111/gcb.14559. CASTRO, F.D.; LOPES, G.R.; BRONDIZIO, E.S. The Brazilian Amazon in times of COVID-19: from crisis to transformation? Ambiente & Sociedade, v. 23, p. 1-9, 2020. doi: http://dx.doi.org/10.1590/1809-4422asoc20200123vu2020l3id. CHAVANA-BRYANT, C.; MALHI, Y.; WU, J.; ASNER, G. P.; ANASTASIOU, A.; ENQUIST, B. J. et al. Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements. New Phytologist, v. 214, n. 3, p. 1049– 1063, 2016. https://doi.org/10.1111/nph.13853. DA ROCHA, H. R.; GOULDEN, M. L.; MILLER, S. D.; MENTON, M. C.; PINTO, L. D. V. O.; DE FREITAS, H. C.; FIGUEIRA, A. M. e S. Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia. Ecological Applications, v.14, n. 4, p. 22– 32, 2004. https://doi.org/10.1890/02-6001. DA ROCHA, H. R.; MANZI, A. O.; CABRAL, O. M.; MILLER, S. D.; GOULDEN, M. L.; SALESKA, S. R.; RESTREPO-COUPE, N.; WOFSY, S.C.; BORMA, L.S.; ARTAXO, P.; VOURLITIS, G.; NOGUEIRA, J. S.; CARDOSO, F. L.; NOBRE, A. D.; KRUIJT, B.; FREITAS, H. C.; VON RANDOW, C.; AGUIAR, R. G.; MAIA J. F. Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil. Journal of Geophysical Research: Biogeosciences, v. 114, n. G1, p. 8, 2009. https://doi.org/10.1029/2007JG000640. DE OLIVEIRA, G.; BRUNSELL, A. N.; MORAES, C. E.; SHIMABUKURO, E. Y.; BERTANI, G.; DOS SANTOS, V. T.; ARAGAO, E. O. C. L. Evaluation of MODIS-based 66 estimates of water-use efficiency in Amazonia. International Journal of Remote Sensing, v. 38, n. 19, p. 5291-5309. 2017. https://doi.org/10.1080/01431161.2017.1339924. DE ROSA, D.; ROWLINGS, D. W.; BIALA, J.; SCHEER, C.; BASSO, B.; GRACE, P. R. N2O and CO2 emissions following repeated application of organic and mineral N fertiliser from a vegetable crop rotation. Science of The Total Environment, v. 637-638, n. 0048-9697, p. 813-824, 2018. https://doi.org/10.1016/j.scitotenv.2018.05.046. DE ROSA, D.; ROWLINGS, D.R.; BIALA, J.; SCHEER, C.; BASSO, B.; MCGREE, J.; GRACE, P.R. Effect of organic and mineral N fertilizers on N2O emissions from an intensive vegetable rotation. Biology and Fertility of Soils, v. 52, p. 895–908, 2016. https://doi.org/10.1007/s00374-016-1117-5 DELGADO, R. C.; PEREIRA, M. G.; TEODORO, P. E.; SANTOS, G. L. DOS; CARVALHO, D. C. DE; MAGISTRALI, I. C.; VILANOVA, R. S. Seasonality of gross primary production in the Atlantic Forest of Brazil. Global Ecology and Conservation, v. 14, p. 1–12, 2018. https://doi.org/10.1016/j.gecco.2018.e00392. DORICH, C. D.; DE ROSA, D.; BARTON, L.; GRACE, P.; ROWLINGS, D.; MIGLIORATI, M. DE A.; WAGNER-RIDDLE, C.; KEY, C.; WANG, D.; FEHR, B.; CONANT, R. T. Global Research Alliance N2O chamber methodology guidelines: Guidelines for gap-filling missing measurements. Journal of Environmental Quality, v. 49, n. 5, p. 1186-1202, 2020. https://doi.org/10.1002/jeq2.20138. DOUGHTY, C. E.; METCALFE, D. B.; GIRARDIN, C.; AMEZQUITA, F.; DURAND, L.; HUASCO, W.; SILVA‐ESPEJO, J. E.; ARAUJO-MURAKAMI, A.; DA COSTA, M. C.; DA COSTA, A. C. L.; ROCHA, W.; MEIR, P.; GALBRAITH, D.; MALHI, Y. Source and Sink Carbon Dynamics and Carbon Allocation in the Amazon Basin. Global Biogeochemical Cycles, v. 29, n. 5, p. 645–655, 2015. doi:10.1002/2014gb005028. FASHAE, A.; OLUSOLA, A. O.; NDUBUISI, I.; UDOMBOSO, C. U. Comparing ANN and ARIMA model in predicting the dischargeof River Opeki from 2010 to 2020 Olutoyin, River Research and Applications, v. 35, n. 2, p. 169–177, 2019. https://doi.org/10.1002/rra.3391. FATICHI, S.; LEUZINGER, S.; KÖRNER, C. Moving beyond photosynthesis: from carbon source to sink-driven vegetation modeling. New Phytologist, v. 201, n. 4, p. 1086– 1095. 2014. https://doi.org/10.1111/nph.12614. HAN, P.; WANG, P. X.; ZHANG, S. Y.; ZHU, D. H. Drought forecasting based on the remote sensing data using ARIMA models, Mathematical and Computer Modelling, v. 51, p. 1398–1403, 2010. https://doi.org/10.1016/j.mcm.2009.10.031. Hirota, M.; Milena Holmgren, M.; Egbert H. Van Nes, E. H.; Scheffer. M. Global Resilience of Tropical Forest and Savanna to Critical Transitions. Science, v. 334, n. 6053, p. 232-235, 2011. doi: 10.1126/science.1210657. INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE). Censo Demográfico, 2010. Disponível em: <www.ibge.gov.br>. Acesso em: 15 de janeiro de 2020. KIM, Y.; KNOX, R. G.; LONGO, M.; MEDVIGY, D.; HUTYRA, L. R.; PYLE, E. H.; WOFSY, C. S.; BRAS, L. R.; MOORCROFT, R. P. Seasonal carbon dynamics and water fluxes in an Amazon rainforest. Global Change Biology, v, 18, n. 4, p. 1322– 1334, 2012. https://doi.org/10.1111/j.1365-2486.2011.02629.x. 67 LI, Z.; AHLSTRÖM, A.; TIAN, F.; GÄRTNER, A.; JIANG, M.; XIA, J. Minimum carbon uptake controls the interannual variability of ecosystem productivity in tropical evergreen forests. Global and Planetary Change, v. 195, p. 103343, 2020. https://doi.org/10.1016/j.gloplacha.2020.103343. LIU, J.; BOWMAN, K. W.; SCHIMEL, D. S.; PARAZOO, N. C.; JIANG, Z.; LEE, M.; BLOOM, A. A.; WUNCH, D.; FRANKENBERG, C.; SUN, Y.; O'DELL, C. W.; GURNEY, K. R.; MENEMENLIS, D.; GIERACH, M.; CRISP, D.; ELDERING, A. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science, v. 358, n. 6360 eaam5690. 2017. https://doi.org/10.1126/science.aam5690. MALHI, Y.; ARAGÃO, O. C. L. E.; METCALFE, D. B.; PAIVA, R.; QUESADA, C. A.; ALMEIDA, S.; ANDERSON, L.; BRANDO, P.; CHAMBERS, J. Q.; Da COSTA, C. L. A.; HUTYRA, R. L.; OLIVEIRA, P.; PATIÑO, S. PYLE, H. E.; ROBERTSON, L. A.; TEIXEIRA, M. L. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests. Global Change Biology, v. 15, n. 5, p. 1255-1274, 2009. https://doi-org.ez30.periodicos.capes.gov.br/10.1111/j.1365-2486.2008.01780.x. MARENGO, J. A.; ESPINOSA, F. C. Extreme seasonal droughts and floods in Amazonia: causes, trends and impacts. International Journal of Climatology, v. 36, n. 3, p. 1033-1050, 2015. doi: 10.1002/joc.4420. National Institute for Space Research (INPE). Disponível em <http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/degrad>. Acesso em: 14 de fevereiro de 2021. PAN, S.; TIAN, H.; DANGAL, S. R.S.; Ouyang, Z.; Tao, B.; Ren, W.; Lu, C.; Running, S. W. Modeling and monitoring terrestrial primary production in a changing global environment: toward a multiscale synthesis of observation and simulation. Advances in Meteorology, v. 2014, n. 6426, p.1 - 17, 2014. doi:10.1155/2014/965936. PAPPAS, C.; PAPALEXIOU, S.M.; KOUTSOYIANNIS, D. A quick gap filling of missing hydrometeorological data. J. Journal of Geophysical Research – Atmospheres, v. 119, p. 9290–9300, 2014. https://doiorg.ez30.periodicos.capes.gov.br/10.1002/2014JD021633. PENG. Y.; GITELSON, A.; SAKAMOTO, T. Remote estimation of gross primary productivity in crops using MODIS 250 m data. Remote Sensing of Environment, v 128, p. 186-196, 2013. DOI:10.1016/j.rse.2012.10.005. Ren, H.; Cromwell, E.; Kravitz, B.; Chen, X. Using Deep Learning to Fill Spatio-Temporal Data Gaps in Hydrological Monitoring Networks. Hydrology and Earth System Sciences, 2019. https://doi.org/10.5194/hess-2019-196. RESTREPO-COUPE N.; DA ROCHA H. R.; DA ARAUJO A. C.; BORMA, L. S.; CHRISTOFFERSEN, B.; CABRAL, O. M. R.; DE CAMARGO, P. B.; CARDOSO, F. L.; DA COSTA, A. C. L.; FITZJARRALD, D. R.; GOULDEN, M. L.; KRUIJT, B.; MAIA, J. M. F.; MALHI, Y. S.; MANZI, A. O.; MILLER, S. D.; NOBRE, A. D.; VON RANDOW, C.; SÁ, LEONARDO D. A.; SAKAI, R. K.; TOTA, J.; WOFSY, S. C.; ZANCHI, F. B.; SALESKA, S. R. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agricultural and Forest Meteorology, v. 182 - 183, n. 0168-1923, p. 128–144, 2013. https://doi.org/10.1016/j.agrformet.2013.04.031. 68 RESTREPO-COUPE, N.; HUETE, A.; DAVIES, K.; CLEVERLY, J.; BERINGER, J.; EAMUS, D.; VAN GORSEL, E.; HUTLEY, L. B.; MEYER, W. S.; MODIS vegetation products as proxies of photosynthetic potential along a gradient of meteorologically and biologically driven ecosystem productivity, Biogeosciences, v. 13, n. 19, p. 5587–5608, 2016. https://doi.org/10.5194/bg-13-5587-2016. SANTOS, R. O. DE.; DELGADO, R. C.; VILANOVA, R. S.; DE SANTANA, R. O.; ANDRADE, C. F DE.; TEODORO, P. E.; SILVA JUNIOR, C. A.; CAPRISTO-SILVA, G. F.; LIMA, M. NMDI application for monitoring different vegetation covers in the Atlantic Forest biome, Brazil. Weather and Climate Extremes, v. 33, n. 100329, 2021. https://doi.org/10.1016/j.wace.2021.100329. SHUTTLEWORTH, W.J. Evaporation from Amazonian rainforest. Proceedings of the Royal Society of London. Series B. Biological Sciences, v. 233, n. 1272, p. 321-346. 1988. https://doi.org/10.1098/rspb.1988.0024. SORRIBAS, M. V.; PAIVA, C. D. R.; MELACK, M. J.; BRAVO, M. J.; JONES, C.; CARVALHO, L.; BEIGHLEY, E.; FORSBERG, B.; HEIL COSTA, H. M. Projections of climate change effects on discharge and inundation in the Amazon basin. Climatic Change, v. 135, p. 555–570, 2016. doi: 10.1007/s10584-016-1640-2. Stocker, B.D.; Zscheischler, J.; Keenan, T.F.; Prentice, I.C.; Seneviratne, S.I.; Peñuelas, J. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nature Geoscience, v. 12, p. 264, 2019. https://doi.org/10.1038/s41561-019-0318-6. VALIPOUR, M. Use of surface water supply index to assess-ing of water resources management in Colorado and Oregon, US. Adv. Agric. Sci. Eng. Res., v. 3, n. 2, p. 631–640, 2013. doi:10.5707/aaser.v3i2.247. VILANOVA, R. S.; DELGADO, R. C.; ABEL, E. L. S.; TEODORO, P. E.; SILVA JUNIOR, C. A; WANDERLEY, H. S.; SILVA, G. F. C. Past and future assessment of vegetation activity for the state of Amazonas-Brazil. Remote Sensing Applications: Society and Environment, v. 17, n. 100278, 2020. https://doi.org/10.1016/j.rsase.2019.100278. VON RANDOW, R. DE C. S.; TOMASELLA, J.; VON RANDOW, C.; DE ARAÚJO, A. C.; MANZI, A. O.; HUTJES, R.; KRUIJT, B. Evapotranspiration and gross primary productivity of secondary vegetation in Amazonia inferred by eddy covariance. Agricultural and Forest Meteorology, v. 294, n. 108141, p. 0168-1923. 2020. https://doi.org/10.1016/j.agrformet.2020.108141. WALTER. O. Y.; KIHORO, J.; A; HENRY; W. K. Imputation of incomplete non- stationary seasonal time series data. Mathematical Theory and Modeling, v. 3, n. 12, p. 2224-5804, 2013. WENG, W.; LUEDEKE, M. K. B.; ZEMP, D. C., LAKES, T.; AND KROPP, J. P. Aerial and surface rivers: downwind impacts on water availability from land use changes in Amazonia. Hydrology and Earth System Sciences, v. 22, n. 1, p. 911–927, 2018. doi: 10.5194/hess-2017-526. WU, C. Y; NIU, Z.; TANG, Q.; HUANG, W. J.; RIVARD B. J. L. Feng Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, v. 149, p. 1015 – 1021, 2009. https://doi.org/10.1016/j.agrformet.2008.12.007. 69 WU, J.; ALBERT, L. P.; LOPES, A. P.; et al. Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science, v. 351, n. 6276, p. 972–976. 2016. doi: 10.1126/science.aad5068. YAN, H.; WANG, S.; HUETE, A.; SHUGART, H. H. Effects of light component and water stress on photosynthesis of Amazon rainforests during the 2015/2016 El Niño drought. Journal of Geophysical Research: Biogeosciences, v. 24, n. 6, p. 1574–1590, 2019. doi:10.1029/2018JG004988. YANG, P.; VAN DER TOL, C. Linking canopy scattering of far-red sun-induced chlorophyll fluorescence with reflectance. Remote Sensing of Environment, v. 209, p. 456– 467, 2018. https://doi.org/10.1016/j.rse.2018.02.029. ZHANG, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, v. 50, p. 159–175, 2003. https://doi.org/10.1016/S0925-2312(01)00702-0.por
dc.subject.cnpqRecursos Florestais e Engenharia Florestalpor
dc.thumbnail.urlhttps://tede.ufrrj.br/retrieve/74940/2021%20-%20Regiane%20Souza%20Vilanova.pdf.jpg*
dc.originais.urihttps://tede.ufrrj.br/jspui/handle/jspui/6963
dc.originais.provenanceSubmitted by Celso Magalhaes (celsomagalhaes@ufrrj.br) on 2023-09-27T15:05:48Z No. of bitstreams: 1 2021 - Regiane Souza Vilanova.pdf: 3646767 bytes, checksum: 6288d9ff78846d13e0caa1b36eb268df (MD5)eng
dc.originais.provenanceMade available in DSpace on 2023-09-27T15:05:48Z (GMT). No. of bitstreams: 1 2021 - Regiane Souza Vilanova.pdf: 3646767 bytes, checksum: 6288d9ff78846d13e0caa1b36eb268df (MD5) Previous issue date: 2021-08-18eng
Appears in Collections:Mestrado em Ciências Ambientais e Florestais

Se for cadastrado no RIMA, poderá receber informações por email.
Se ainda não tem uma conta, cadastre-se aqui!

Files in This Item:
File Description SizeFormat 
2021 - Regiane Souza Vilanova.pdf2021 - Regiane Souza Vilanova3.56 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.