1. 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
- Author
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I. B. Sa, Ieda Del’Arco Sanches, Lênio Soares Galvão, T. A. Taura, and Hilton Luís Ferraz da Silveira
- Subjects
Global and Planetary Change ,010504 meteorology & atmospheric sciences ,0211 other engineering and technologies ,02 engineering and technology ,Enhanced vegetation index ,Vegetation ,Management, Monitoring, Policy and Law ,01 natural sciences ,Normalized Difference Vegetation Index ,Soil survey ,Field capacity ,Soil water ,Cation-exchange capacity ,Environmental science ,Physical geography ,Computers in Earth Sciences ,Soil fertility ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Earth-Surface Processes - 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.
- Published
- 2018
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