A tool for fast creation of vegetation index time series in GEE

geoindexity provides an accesible fast forward way to create time series of vegetation indices for user defined AOIs and time periods.It is based on Google Earth Engine (GEE). Its main time series class Geoindexity is used to filter GEE image collections (Sentinel 2 SRF Harmonized, Landsat), calculating indices,plotting, and exporting data and plots to Google Drive.

The Landast collection operational

About the project

This package was developed by the students Rohit Khati, David Hansen, Gernot Nikolaus and Asad Ullah as part of the Software Development (Python) course at the University of Salzburg in the summer term 2024.

Requirements

To use this script, you need:

• Access to Google Earth Engine (GEE) with a valid account and authentication.
• Python libraries: Earth Engine Python API (ee), Pandas (pd), Matplotlib (plt), and NumPy (np).

The environmental.yml file allows to create a conda environment with all dependencies installed.

Installation

The package is not deployed to PyPI yet. However, you can install the package in your prefered package management system like conda that allows for packagae installation using pip.

pip workflow

Use the following command to install Geoindexity using pip:

pip install git+https://github.com/ro-hit81/GeoIndexity

conda workflow

Activate your prefered conda environment:

conda activate YOUR_ENVIRONMENT

If pipis not installed yet use:

conda install pip 

After pip is installed, use the command from above:

pip install git+https://github.com/ro-hit81/GeoIndexity

Uninstall package

To uninstall use the following pip command:

pip uninstall geoindexity 

Documentation

Find full documentation here

Working example

Import dependencies:

import ee 
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt 

Initialize and authenticate GEE:

ee.Initialize() 
ee.Authenticate() 

Import geoindexity:

import geoindexity.geoindexity as gx

roi = [
    12.90,
    47.75,
    13.20,
    47.85,
]
start_date = '2020-01-01'
end_date  = '2020-03-20'
properties = {
    'CLOUDY_PIXEL_PERCENTAGE':10
}

ts = gx.Geoindexity(roi, start_date, end_date, properties=properties, collection_id='Sentinel')

ts.ndvi_collection() 
ts.reduce_ndvi_mean()
ts.plot()