LINDEX is a containerized Landsat 8 processing tool for timeseries index analysis.
A directory containing compressed Landsat 8 data, and corner cordinates for your region of interest.
The origional Landast 8 data cropped to your region of interest and sorted by cloudcover as well as the results of the selected analysis for each non-cloudy day.
- Positional Arguments:
- Directory containing compressed Landsat 8 data: 'indir'
- Required Arguments:
- Corner cordinates of GPS bounding box for your region of intrest in UTM: '-b', '--bounding_box'
- Index you would like to run (options below): '-in', '--index'
- Optional Arguments:
- Strictness of cloud detection: '-c', '--how_strict', default = 0.7 (the lower the value the more strict)
- Download the Earth Explorer Bulk Download Application
- Use the Bulk Download Application and the Earth Eplorer Website in order to bulk download compressed Lansat-8 data in your region of intrest
- Uncompress one TAR
- Use QGIS to visualize one band
- Zoom into your ROI
- Use the QGIS 'Lat Lon Tools' plugin to copy the canvas coordinates (modify plugin setting to take coordinates in UTM with spaces separating)
- Run the container
singularity build lindex.img docker://travissimmons/lindex:all_indices
singularity run lindex.img {PATH TO COMPRESSED DATA} -b {PASTE CORNER COORDINATES} -in {index name}
| Index Name | Formula |
|---|---|
| Normalized Difference Water Index (NDWI) | (NIR-SWIR)/(NIR+SIWR) |
| Normalized Differencce Vegetation Index (NDVI) | (NIR-RED)/(NIR+RED) |
| Enhanced Vegetation Index (EVI) | G*((NIR-RED)/(NIR+C1R-C2BLUE+L)) |
| Advanced Vegetation Index (AVI) | (NIR*(1-RED)*(NIR-RED))^(1/3) |
| Soil Adjusted Vegetation IndexSAVI | ((NIR-RED)/(NIR+RED+L))*(1+L) |
| NDMI | (NIR-SWIR)/(NIR+SWIR) |
| MSI | MidIR/NIR |
| GCI | (NIR)/(GREEN)-1 |
| NBRI | (NIR-SWIR)/(NIR+SWIR) |
| BSI | ((RED+SWIR)-(NIR+BLUE))/((RED+SWIR)+(NIR+BLUE)) |
| NDSI | (GREEN-SWIR)/(GREEN+SWIR) |
| NDGI | (NIR-GREEN)/(NIR+GREEN) |
Refer to the index template function in lindex.py in order to add your own custom index.
DOI: 10.1002/essoar.10511799.1