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@Article{SilveiraGalSanSáTau:2018: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 = "2018",
               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",
             language = "en",
           targetfile = "silveira_use.pdf",
        urlaccessdate = "24 nov. 2020"
}


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