Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/974172
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorGALVÍNCIO, J. D.pt_BR
dc.contributor.authorMOURA, M. S. B. dept_BR
dc.contributor.authorSILVA, T. G. F. dapt_BR
dc.contributor.authorSILVA, B. B. dapt_BR
dc.contributor.authorNAUE, C. R.pt_BR
dc.date.accessioned2013-12-17T11:11:11Zpt_BR
dc.date.available2013-12-17T11:11:11Zpt_BR
dc.date.created2013-12-17pt_BR
dc.date.issued2013pt_BR
dc.identifier.citationInternational Journal of Remote Sensing Applications, v. 3, n. 4, p. 193-202, dec. 2013.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/974172pt_BR
dc.descriptionSavannas are globally important ecosystems of great significance to human economies. Savannas exist in water-limited regions which forces tree canopies open and heterogeneous. The open canopy structure allows grass to co-dominate in the savannas by occupying different niches in space and time. Leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) characterize vegetation canopy functioning and energy absorption capacity. LAI and FPAR are key parameters in most ecosystem productivity models and global models of climate, hydrology, biogeochemistry and ecology. Given the above, this study aimed to develop an equation of LAI calibrated by savannah in semiarid northeastern Brazil and proposed a model to better estimate the LAI for dry forest, such as the savanna (Caatinga). The model developed in this study may be used to improve the estimates of Leaf Area Index [LAI] in dry forest with NDVI. One model for savanna-specific of leaf area index (LAI) has been developed. The use of S Curve statistical methods to calibrate the leaf area index (LAI) proved to be an efficient method. The model development gives good results in most of the LAI range known for Caatinga stands in Northeast of Brazil. The Root Mean Square Error (RMSE) calculated on an independent LAI dataset was 0.10, which is about 6% of the average measured LAI. This method offers a simple and operational alternative to application of complex and computationally intensive techniques, and could be used to design other species-specific LAIs. This study reinforces the importance of developing models to better estimate the LAI in different ecosystems since there are no similarities of the LAI between dry and humid climate.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectLAIpt_BR
dc.subjectEcossistemas secospt_BR
dc.subjectModelo de desenvolvimentopt_BR
dc.subjectFieldspecpt_BR
dc.subjectSavanaspt_BR
dc.subjectNatural resourcept_BR
dc.titleLAI Improved to dry forest in Semiarid of the Brazil.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2013-12-20T11:11:11Zpt_BR
dc.subject.thesagroRecurso naturalpt_BR
dc.subject.thesagroSensoriamento remotopt_BR
dc.subject.thesagroCaatingapt_BR
riaa.ainfo.id974172pt_BR
riaa.ainfo.lastupdate2013-12-20pt_BR
dc.identifier.doi10.14355/ijrsa.2013.0304.04pt_BR
dc.contributor.institutionJOSICLÊDA DOMICIANO GALVÍNCIO; MAGNA SOELMA BESERRA DE MOURA, CPATSA; THIERES GEORGE FREIRE DA SILVA; BERNARDO BARBOSA DA SILVA; CARINE ROSA NAUE.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CPATSA)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
Magna3.pdf901.8 kBAdobe PDFThumbnail
Visualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace