landsat8

R Package to make Landsat8 data accessible

Practical split-window algorithm estimating Land Surface Temperature from Landsat 8 OLI/TIRS imagery

Python function that allows you to subset Landsat Collection 2 metadata (by row, path, tier, cloud cover, dates, and mission) and save image IDs in a txt file for later ingestion by M2M Landsat API. Compatible with Landsat 4,5,7,8 Level 1 and Level 2.

Extracting topography from mountain glaciers, through the use of shadow casted by surrounding mountains.

transform MTL.txt to MTL.xml of Landsat8 datas

Multi-temporal Landsat 8 image-processing workflow for flood monitoring in the Inner Niger Delta, Mali (2013-2022): R/terra vegetation indices (NDVI, EVI, SAVI), RStoolbox/GRASS unsupervised k-means classification, Python Pearson/Kendall correlograms and a GMT map. Lemenkova & Debeir (2023), Artificial Satellites 58(4):278-313.

R/terra scripts computing multispectral vegetation and water indices (NDVI, GNDVI, IPVI, NDWI, OSAVI) from Landsat 8 OLI/TIRS surface reflectance over the Khartoum region of Sudan for 2013/2018/2022, plus a GMT study-area map. Lemenkova & Debeir (2023), J. Imaging 9(5):98.

various freelance projects

R script for loading, inspecting and visualising a Landsat 8 OLI/TIRS scene with raster/sp/rgdal: band I/O, CRS/grid inspection, NIR reflectance histogram, class-break mapping, band stacking and true-/false-colour composites. Example: Rio Piranhas, Brazil.

R scripts for unsupervised k-means classification (RStoolbox) and NDVI computation of Landsat OLI/TIRS imagery over the Congo River Basin (Basoko, Kisangani), DRC, with colour composites and a terra tutorial series. Lemenkova & Debeir (2022), Applied Sciences 12(24):12554.

R/terra scripts computing multi-temporal NDVI (Normalized Difference Vegetation Index) for South Sudan from Landsat 8/9 surface-reflectance bands, one per year 2014-2023, with RdYlGn mapping and NDVI histograms. Companion to Lemenkova (2023), Analytics 2(3):745-780 (Sudd Wetlands, South Sudan).