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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido; Embrapa Solos. |
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Data corrente: |
02/07/2018 |
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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: |
SILVEIRA, H. L. F. da; GALVÃO, L. S.; SANCHES, I. D.; SA, I. B.; TAURA, T. A. |
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Afiliação: |
HILTON LUIS FERRAZ DA SILVEIRA, CNPS; LÊNIO SOARES GALVÃO, INPE; IEDA DEL'ARCO SANCHES, INPE; IEDO BEZERRA SA, CPATSA; TATIANA AYAKO TAURA, CPATSA. |
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Título: |
Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 73, p. 179-190, Dec. 2018. |
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DOI: |
https://doi.org/10.1016/j.jag.2018.06.016 |
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Idioma: |
Inglês |
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Conteúdo: |
The Caatinga is an important ecosystem in the semi-arid region of northeast Brazil and a natural laboratory for the study of plant adaptation to seasonal water stress or prolonged droughts. The soil water availability for plants depends on plant root depth and soil properties. Here, we combined for the first time the remote sensing classification of Caatinga physiognomies with soil information derived from geostatistical analysis to relate vegetation distribution with physico-chemical attributes of soils. We evaluated the potential of multi-temporal data acquired by the MultiSpectral Instrument (MSI)/Sentinel-2 for Random Forest (RF) classification of seven physiognomies. In addition, we analyzed the contribution of airborne LiDAR metrics to improve classification accuracy compared to six vegetation indices (VIs) and 10 reflectance bands from the MSI instrument. Using a detailed soil survey, the spatial distribution of the vegetation physiognomies mapped by RF was associated with the variability of 20 physico-chemical attributes of 75 soil profiles submitted to principal components analysis (PCA) and ordinary kriging. The results showed gains in overall classification accuracy with use of the multitemporal data over the mono-temporal observations. Gains in classification of arboreous Caatinga were also observed after the insertion of LiDAR metrics in the analysis, especially the percentage of vegetation cover with height greater than 5 m, the terrain elevation and the standard deviation of vegetation height. Overall, the most important metrics for classification were the VIs, especially the Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII-1), Optimized Soil-Adjusted Vegetation Index (OSAVI) and the Normalized Difference Vegetation Index (NDVI). The most important MSI/Sentinel-2 bands were positioned in the red-edge spectral interval. From PCA, soil attributes responsible for most of the data variance were related to soil fertility, soil depth and rock fragments in the surface horizon. The amounts of gravels and pebbles were factors of physiognomic variability with shrub and sub-shrub Caatinga occurring preferentially over shallow and stony soils. By contrast, arboreous Caatinga occurred over soils with total profile depth greater than 1 m. Finally, areas of sub-shrub Caatinga had greater values of cation exchange capacity (CEC) and water retention at field capacity. than areas of arboreous Caatinga. The differences were statistically significant at 95% confidence level, as indicated by Mann-Whitney U tests. MenosThe Caatinga is an important ecosystem in the semi-arid region of northeast Brazil and a natural laboratory for the study of plant adaptation to seasonal water stress or prolonged droughts. The soil water availability for plants depends on plant root depth and soil properties. Here, we combined for the first time the remote sensing classification of Caatinga physiognomies with soil information derived from geostatistical analysis to relate vegetation distribution with physico-chemical attributes of soils. We evaluated the potential of multi-temporal data acquired by the MultiSpectral Instrument (MSI)/Sentinel-2 for Random Forest (RF) classification of seven physiognomies. In addition, we analyzed the contribution of airborne LiDAR metrics to improve classification accuracy compared to six vegetation indices (VIs) and 10 reflectance bands from the MSI instrument. Using a detailed soil survey, the spatial distribution of the vegetation physiognomies mapped by RF was associated with the variability of 20 physico-chemical attributes of 75 soil profiles submitted to principal components analysis (PCA) and ordinary kriging. The results showed gains in overall classification accuracy with use of the multitemporal data over the mono-temporal observations. Gains in classification of arboreous Caatinga were also observed after the insertion of LiDAR metrics in the analysis, especially the percentage of vegetation cover with height greater than 5 m, the terrain elevation and the standa... Mostrar Tudo |
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Palavras-Chave: |
Bioma Caatinga; Fisionomia da Caatinga. |
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Thesagro: |
Caatinga; Cobertura do Solo; Cobertura Vegetal; Erosão do Solo; Relação Solo-Planta; Retenção de Água no Solo; Revegetação; Sensoriamento Remoto; Solo; Vegetação Nativa. |
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Thesaurus Nal: |
Ecosystems; Forest ecosystems; Vegetation; Vegetation types. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 03790naa a2200373 a 4500
001 2093256
005 2021-11-11
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.jag.2018.06.016$2DOI
100 1 $aSILVEIRA, H. L. F. da
245 $aUse of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region.$h[electronic resource]
260 $c2018
520 $aThe Caatinga is an important ecosystem in the semi-arid region of northeast Brazil and a natural laboratory for the study of plant adaptation to seasonal water stress or prolonged droughts. The soil water availability for plants depends on plant root depth and soil properties. Here, we combined for the first time the remote sensing classification of Caatinga physiognomies with soil information derived from geostatistical analysis to relate vegetation distribution with physico-chemical attributes of soils. We evaluated the potential of multi-temporal data acquired by the MultiSpectral Instrument (MSI)/Sentinel-2 for Random Forest (RF) classification of seven physiognomies. In addition, we analyzed the contribution of airborne LiDAR metrics to improve classification accuracy compared to six vegetation indices (VIs) and 10 reflectance bands from the MSI instrument. Using a detailed soil survey, the spatial distribution of the vegetation physiognomies mapped by RF was associated with the variability of 20 physico-chemical attributes of 75 soil profiles submitted to principal components analysis (PCA) and ordinary kriging. The results showed gains in overall classification accuracy with use of the multitemporal data over the mono-temporal observations. Gains in classification of arboreous Caatinga were also observed after the insertion of LiDAR metrics in the analysis, especially the percentage of vegetation cover with height greater than 5 m, the terrain elevation and the standard deviation of vegetation height. Overall, the most important metrics for classification were the VIs, especially the Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII-1), Optimized Soil-Adjusted Vegetation Index (OSAVI) and the Normalized Difference Vegetation Index (NDVI). The most important MSI/Sentinel-2 bands were positioned in the red-edge spectral interval. From PCA, soil attributes responsible for most of the data variance were related to soil fertility, soil depth and rock fragments in the surface horizon. The amounts of gravels and pebbles were factors of physiognomic variability with shrub and sub-shrub Caatinga occurring preferentially over shallow and stony soils. By contrast, arboreous Caatinga occurred over soils with total profile depth greater than 1 m. Finally, areas of sub-shrub Caatinga had greater values of cation exchange capacity (CEC) and water retention at field capacity. than areas of arboreous Caatinga. The differences were statistically significant at 95% confidence level, as indicated by Mann-Whitney U tests.
650 $aEcosystems
650 $aForest ecosystems
650 $aVegetation
650 $aVegetation types
650 $aCaatinga
650 $aCobertura do Solo
650 $aCobertura Vegetal
650 $aErosão do Solo
650 $aRelação Solo-Planta
650 $aRetenção de Água no Solo
650 $aRevegetação
650 $aSensoriamento Remoto
650 $aSolo
650 $aVegetação Nativa
653 $aBioma Caatinga
653 $aFisionomia da Caatinga
700 1 $aGALVÃO, L. S.
700 1 $aSANCHES, I. D.
700 1 $aSA, I. B.
700 1 $aTAURA, T. A.
773 $tInternational Journal of Applied Earth Observation and Geoinformation$gv. 73, p. 179-190, Dec. 2018.
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Registro original: |
Embrapa Solos (CNPS) |
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