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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
05/11/2019 |
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Data da última atualização: |
11/11/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
PINHEIRO, H. S. K.; BARBOSA, T. P. R.; ANTUNES, M. A. H.; CARVALHO, D. C. de; NUMMER, A. R.; CARVALHO JUNIOR, W. de; CHAGAS, C. da S.; FERNANDES-FILHO, E. I.; PEREIRA, M. G. |
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Afiliação: |
HELENA 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. |
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Título: |
Assessment of phytoecological variability by red-edge spectral indices and soil-landscape relationships. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Remote Sensing, v. 11, n. 20, 2448, 2019. |
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DOI: |
https://doi.org/10.3390/rs11202448 |
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Idioma: |
Inglês |
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Conteúdo: |
There 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. MenosThere 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 a... Mostrar Tudo |
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Thesagro: |
Conservação; Recurso Natural; Sensoriamento Remoto. |
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Thesaurus Nal: |
Conservation areas; Remote sensing. |
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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/204270/1/Assessment-of-phytoecological-variability-by-red-edge-spectral-indices-and-soil-landscape-relationships-2019.pdf
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Marc: |
LEADER 02609naa a2200289 a 4500
001 2113915
005 2021-11-11
008 2019 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs11202448$2DOI
100 1 $aPINHEIRO, H. S. K.
245 $aAssessment of phytoecological variability by red-edge spectral indices and soil-landscape relationships.$h[electronic resource]
260 $c2019
520 $aThere 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.
650 $aConservation areas
650 $aRemote sensing
650 $aConservação
650 $aRecurso Natural
650 $aSensoriamento Remoto
700 1 $aBARBOSA, T. P. R.
700 1 $aANTUNES, M. A. H.
700 1 $aCARVALHO, D. C. de
700 1 $aNUMMER, A. R.
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aCHAGAS, C. da S.
700 1 $aFERNANDES-FILHO, E. I.
700 1 $aPEREIRA, M. G.
773 $tRemote Sensing$gv. 11, n. 20, 2448, 2019.
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Embrapa Solos (CNPS) |
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| 1. |  | PINHEIRO, H. S. K.; BARBOSA, T. P. R.; ANTUNES, M. A. H.; CARVALHO, D. C. de; NUMMER, A. R.; CARVALHO JUNIOR, W. de; CHAGAS, C. da S.; FERNANDES-FILHO, E. I.; PEREIRA, M. G. Assessment of phytoecological variability by red-edge spectral indices and soil-landscape relationships. Remote Sensing, v. 11, n. 20, 2448, 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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