Potential use of R-statistical programming in the field of geoscience

Bishwal, R. M.

doi:10.1109/i2ct.2017.8226275

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…They contain a wide variety of mathematical statistical tools, from the very basic functions to the highly sophisticated algorithms of data analysis [81]. Recent advances in geospatial data processing [82] and image classification [83,84] have made R successful for remote sensing data processing, including image classification and segmentation. This poses a new way to process and model satellite images and derive spatially structured information from complex scenes.…”

Section: Related Workmentioning

confidence: 99%

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Lemenkova

Debeir

2022

J. Imaging

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In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d’Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and `raster’ and `terra’ packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d’Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d’Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d’Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing.

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Section: Related Workmentioning

confidence: 99%

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Lemenkova

Debeir

2022

J. Imaging

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“…Geoscience awaits the advent of rapid algorithms capable of analysing the mineral composition of rocks [6] and corresponding geometry of rock-forming components [7,8]. In all cases, the intention behind developing new solutions is to enhance the reproducibility of the research, minimize subjectivity and reduce time and costs [9].…”

Section: Introductionmentioning

confidence: 99%

Decoding Rocks: An Assessment of Geomaterial Microstructure Using X-ray Microtomography, Image Analysis and Multivariate Statistics

et al. 2021

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An understanding of the microstructure of geomaterials such as rocks is fundamental in the evaluation of their functional properties, as well as the decryption of their geological history. We present a semi-automated statistical protocol for a complex 3D characterization of the microstructure of granular materials, including the clustering of grains and a description of their chemical composition, size, shape, and spatial properties with 44 unique parameters. The approach consists of an X-ray microtomographic image processing procedure, followed by measurements using image analysis and statistical multivariate analysis of its results utilizing freeware and widely available software. The statistical approach proposed was tested out on a sandstone sample with hidden and localized deformational microstructures. The grains were clustered into distinctive groups covering different compositional and geometrical features of the sample’s granular framework. The grains are pervasively and evenly distributed within the analysed sample. The spatial arrangement of grains in particular clusters is well organized and shows a directional trend referring to both microstructures. The methodological approach can be applied to any other rock type and enables the tracking of microstructural trends in grains arrangement.

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R software packages as a tool for evaluating soil physical and hydraulic properties

Sousa

Rodrigues

Lima

et al. 2020

Computers and Electronics in Agriculture

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Potential use of R-statistical programming in the field of geoscience

Cited by 5 publications

References 3 publications

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Decoding Rocks: An Assessment of Geomaterial Microstructure Using X-ray Microtomography, Image Analysis and Multivariate Statistics

R software packages as a tool for evaluating soil physical and hydraulic properties

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