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

• Published in: Journal of imaging Vol. 8; no. 12; p. 317
• Main Authors: Lemenkova, Polina, Debeir, Olivier
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.11.2022; MDPI
• Subjects: Algorithms; Automation; Big Data; Classification; Computer Aided Engineering; Computer Science; Data analysis; Data processing; Data science; Digital Elevation Models; Electronic data processing; Environmental monitoring; Geographic information systems; Graphics; Image Processing; Information Retrieval; Landsat satellites; Libraries; Methods; Modeling and Simulation; Modelling; Normalized difference vegetative index; programming; Programming Languages; Python; Python (Programming language); R (Programming language); Regional analysis; Remote sensing; Resampling; satellite image; Satellite imagery; Satellite imaging; Signal and Image Processing; Software; Spatial analysis; Spatial data; Statistical analysis; Terrain analysis; Terrain models; Topography; Vegetation; Côte d'Ivoire; image processing; R; mapping; spatial analysis; digital terrain model; remote sensing; cartography; satellite image; programming; Python; Image processing; geoinformatics; Africa; image analysis; Computer science
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging8120317

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.