As Earth's ecological landscape continues to change, it will become
increasingly important to understand how it has changed, and how it may
change in the future. Freely available multispectral remote sensing
datasets, such as the Landsat-8 dataset accessed through the USGS
EarthExplorer tool, provide large scale, high resolution satellite
imagery that can be leveraged by researchers across scientific
disciplines for timeseries index analysis. LINDEX provides an extensible
framework that automates Landsat-8 timeseries index analysis, resulting
in an average ninety-four percent reduction in overall processing time
compared to hands-on methods. Traditionally, in order to make use of
this historical data, researchers must acquire the data, uncompress each
scene, crop each scene to their region of interest, sort each cropped
scene by date and cloud cover, and then either use GIS tools to run
index analyses or code the index analyses themselves. This process is
time consuming, requires extensive computational knowledge, and is prone
to human error. In order to address these challenges, we developed
LINDEX, a fully containerized end-to-end extensible processing framework
for Landsat-8 timeseries index analysis. LINDEX is open source and
leverages open source python packages to automate decompression,
cropping, cloud cover detection, sorting by date, and index analysis for
Landsat-8 data while also providing a customisable and growing library
of eleven ecologically useful indices including NDWI, NDMI, NDSI and
NDGI. LINDEX is designed to work synergistically with the EarthExplorer
Bulk Download Application as well as QGIS, providing a bridge from
download through analysis. The LINDEX framework uses a well defined
methodology for incorporating custom indices into your workflow, making
LINDEX a useful tool for researchers interested in exploring any
Landsat-8 multispectral index while saving time, and reducing error.