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

ABSTRACT: 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 veget

SUBMITTER: Lemenkova P 

PROVIDER: S-EPMC9786221 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature