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Python tools for generating vegetation index timeseries from PhenoCam images.

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Python tools for generating vegetation index timeseries from PhenoCam images.

  • Free software: MIT license

Introduction

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_timeseries
  • generate_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.

Quick Installation

From the command line type:

pip install vegindex

Some 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 vegindex

The latest version of the documentation can be found at readthedocs.io

Changelog

0.6.1 (2019-07-15)

  • Update installation doc.
  • Simple test for reading roistats file.

0.6.0 (2019-07-12)

  • Add awbflag (auto white-balance) to roistats file

0.5.3 (2019-04-03)

  • Update requirements for python 3.7
  • Add prefix to regular expressions

0.5.2 (2018-04-09)

  • Really fix bug in plot_roistats when no data are filtered.

0.5.1 (2018-04-09)

  • Fix bug in plot_roistats when no data are filtered.
  • Update docs

0.5.0 (2017-11-29)

  • Fix header on roistats.csv file
  • Add plotting script (matplotlib library is now required)
  • Remove timeout on requests query which was causing tests to fail.
  • Update usage docs

0.4.0 (2017-11-27)

  • Add fallback to local site_info.csv file to get basic site metadata
  • Update exception handling (removed bare exceptions)

0.3.1 (2017-10-06)

  • Change data product name from _roi_statistics.csv to _roistats.csv

0.3.0 (2017-09-12)

  • Added support for .meta files
  • Change data product name from _timeseries.csv to _roi_statistics.csv

0.2.0rc1 (2017-06-14)

  • Added support for python3

0.1.1rc3 (2017-06-13)

  • First release on PyPI.

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