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dc.contributor.authorPINHEIRO, H. S. K.eng
dc.contributor.authorBARBOSA, T. P. R.eng
dc.contributor.authorANTUNES, M. A. H.eng
dc.contributor.authorCARVALHO, D. C. deeng
dc.contributor.authorNUMMER, A. R.eng
dc.contributor.authorCARVALHO JUNIOR, W. deeng
dc.contributor.authorCHAGAS, C. da S.eng
dc.contributor.authorFERNANDES-FILHO, E. I.eng
dc.contributor.authorPEREIRA, M. G.eng
dc.date.accessioned2019-11-06T00:38:22Z-
dc.date.available2019-11-06T00:38:22Z-
dc.date.created2019-11-05
dc.date.issued2019
dc.identifier.citationRemote Sensing, v. 11, n. 20, 2448, 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1113915-
dc.descriptionThere is a relation of vegetation physiognomies with soil and geological conditions that can be represented spatially with the support of remote sensing data. The goal of this research was to map vegetation physiognomies in a mountainous area by using Sentinel-2 Multispectral Instrument (MSI) data and morphometrical covariates through data mining techniques. The research was based on red-edge (RE) bands, and indices, to classify phytophysiognomies at two taxonomic levels. The input data was pixel sampled based on field sample sites. Data mining procedures comprised covariate selection and supervised classification through the Random Forest model. Results showed the potential of bands 3, 5, and 6 to map phytophysiognomies for both seasons, as well as Green Chlorophyll (CLg) and SAVI indices. NDVI indices were important, particularly those calculated with bands 6, 7, 8, and 8A, which were placed at the RE position. The model performance showed reasonable success to Kappa index 0.72 and 0.56 for the first and fifth taxonomic level, respectively. The model presented confusion between Broadleaved dwarf-forest, Parkland Savanna, and Bushy grassland. Savanna formations occurred variably in the area while Bushy grasslands strictly occur in certain landscape positions. Broadleaved forests presented the best performance (first taxonomic level), and among its variation (fifth level) the model could precisely capture the pattern for those on deep soils from gneiss parent material. The approach was thus useful to capture intrinsic soil-plant relationships and its relation with remote sensing data, showing potential to map phytophysiognomies in two distinct taxonomic levels in poorly accessible areas.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.titleAssessment of phytoecological variability by red-edge spectral indices and soil-landscape relationships.eng
dc.typeArtigo de periódicoeng
dc.date.updated2019-11-08T11:11:11Z
dc.subject.thesagroSensoriamento Remotoeng
dc.subject.thesagroConservaçãoeng
dc.subject.thesagroRecurso Naturaleng
dc.subject.nalthesaurusRemote sensingeng
dc.subject.nalthesaurusConservation areaseng
riaa.ainfo.id1113915eng
riaa.ainfo.lastupdate2019-11-08 -02:00:00
dc.identifier.doihttps://doi.org/10.3390/rs11202448eng
dc.contributor.institutionHELENA S. K. PINHEIRO, UFRRJ; THERESA P. R. BARBOSA, UFRRJ; MAURO A. H. ANTUNES, UFRRJ; DANIEL COSTA DE CARVALHO, UnB; ALEXIS R. NUMMER, UFRRJ; WALDIR DE CARVALHO JUNIOR, CNPS; CESAR DA SILVA CHAGAS, CNPS; ELPÍDIO I. FERNANDES-FILHO, UFV; MARCOS GERVASIO PEREIRA, UFRRJ.eng
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