@Article{SilveiraGalSanSáTau:2019:UsMSAi,
author = "Silveira, Hilton Lu{\'{\i}}s Ferraz da and Galv{\~a}o,
L{\^e}nio Soares and Sanches, Ieda Del'Arco and S{\'a}, Iedo
Bezerra de and Taura, Tatiana Ayako",
affiliation = "{Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Empresa Brasileira de
Pesquisa Agropecu{\'a}ria (EMBRAPA)} and {Empresa Brasileira de
Pesquisa Agropecu{\'a}ria (EMBRAPA)}",
title = "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",
journal = "International Journal of Applied Earth Observation and
Geoinformation",
year = "2019",
volume = "73",
pages = "179--190",
month = "Dec.",
keywords = "Caatinga, Random forest, Classification, Principal components
analysis, Kriging.",
abstract = "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 multi-temporal 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.",
doi = "10.1016/j.jag.2018.06.016",
url = "http://dx.doi.org/10.1016/j.jag.2018.06.016",
issn = "0303-2434",
label = "self-archiving-INPE-MCTIC-GOV-BR",
language = "en",
targetfile = "Silveira_1-s2.0-S0303243418304483-main.pdf",
urlaccessdate = "03 jun. 2024"
}