Python tools for generating vegetation index timeseries from PhenoCam images.
- Free software: MIT license
The PhenoCam Network is a project designed to study the patterns of seasonal variation (phenology) of vegetation. The project website is at https://phenocam.sr.unh.edu/. The network consists of many cameras collecting images of various types of vegetation. By analysing the images we can quantify the seasonal changes at a particular camera site.
A "vegetation index" refers to a quantity calculated using information from various spectral bands of an image of a vegetated area. The image is typically obtained from a remote-sensing instrument on an earth orbiting satellite. There are several vegetation index values in common usage. The most widely used are NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index). For the PhenoCam project the Green Chromatic Coordinate or GCC is our standard vegetation index.
For the PhenoCam network, the images are obtained from webcams (usually installed on towers) looking across a vegetated landscape. These images are typically in JPEG format and have 3-bands (Red, Green, and Blue). For some cameras a separate image dominated by an IR (infrared) band is collected.
The algorithms used in in this package have been discussed in numerous publications. You can find a list of publications for the PhenoCam Network project here. The details of the calculation of GCC are presented in this jupyter notebook .
After the package is installed two python scripts should be available:
generate_roi_timeseriesgenerate_summary_timeseries
These scripts allow you to reproduce the PhenoCam network "standard timeseries products" from downloaded data. For a description of the products see the project Tools Page.
From the command line type:
pip install vegindexSome of the packages that vegindex depends on may not install automatically (using pip) since they depend on system libraries. If the above command does not work you can try:
pip install Pillow
pip install vegindexThe latest version of the documentation can be found at readthedocs.io