Skip to main content

Python package to create ndvi seasonal composites, and download them as gif and geotiff

Project description

ndvi2gif is a python script for creating seasonal NDVI compositions gifs. This is just a small python class that uses Google Earth Engine API and the amazing Geemap package, to create yearly compositions based on the maximum NDVI value reached in every seasons. Maximum NDVI is used as season reducer, in order to avoid clouds and cloud shadows. However, we have added ‘perc_90’ (percentile 90) and ‘median’ as others available statistic choice when instantiating the class. Max remains the default, but sometimes median gives a better visual result, specially with Landsat 4 and 5 that sometimes has band errors that can affect NDVI results. Percentile 90 is a good compromise between max and median.

So, this process generates a raster with 4 bands (Winter, Spring, Summer and Autumn) for every year in the chosen time period.

Satellite collections available are:

  • Sentinel 1-C-band Synthetic Aperture Radar 10 m pixel VH cross-polarization (‘sar’) | 2014-10-03 - Present
  • Sentinel 2-MSI 10 m pixel NDVI (‘Sentinel’) | 2015-06-23 - Present
  • Landsat 4-TM, 5-TM, 7-ETM+ and 8-OLI 30 m pixel NDVI (‘Landsat’) | 1982 - Present
  • MODIS MOD09Q1 v006 250 m pixel NDVI (‘MODIS’) | 2000-03-05 - Present

Landsat collections datasets and MODIS, are Surface Reflectance (SR) data, while Sentinel 2 is Top of Atmosphere Reflectance (TOA) dataset. This is because Surface Reflectance for Sentinel 2 data, is only available since 2017.

If everything runs well, you should get a GIF similar to those ones that you can find in the pics folder of this repo. Actually, you will get 2 gifs, one of them named “mygif_texted.gif”, which add year as text to the gif. Here you can see an example close to Seville, where you can tell the blue colours (blue band in this example is summer) showing paddy fields over a marsh area (summer crops). Outside the marshes, the colours green and yellow predominate,showing winter crops such as cereals. You can also realize the intermediate colours for different crops.

https://i.imgur.com/UZDptan.gif

Beyond the nice gif, a lot of information can be obtained with this kind of multi seasonal NDVI approach. Knowing the pair NDVI season-Raster band that you chose for your gif, and having colour formation in mind (graphic below), you could tell which is the phenology, and therfore the crop or every parcel, and even how it changes through the years. White colours means high NDVI values for the three chosen seasons (perennial vegetation), black colour means low NDVI values, such as permanent water bodies, sand, impervious surfaces, etc…

Since we have added SAR data, maybe is no longer correct saying this is an NDVI tool, but with SAR the meaning s very similar to the NDVI approach, in this case we get higher return values when plants are bigger, and very low values for baresoil. So, at the end is another way to have a multi-temporal look at crop growth.

https://i.imgur.com/tq4aMBv.jpg

Last, you have the choice to download the yearly ndvi composites as tiff files into your computer, in case you want the data for further analysis. Also, it have been noticed that Google Earth Engine reducers are really nice to create gorgeous multi-year composties, even for very large areas with MODIS, e.g. median seasonal NDVI for whole Africa between 2001 and 2020. So, besides the automatic export for each year, you also have the chance to export your favourite multi-year compostion in a single file.

Installation

This tiny and humble python class depends on geemap, so geemap will be installed for you. Also it could be a good idea install first geemap in a python environment (you can see the details here: geemap install) and later install ndvi2gif in that environment via pip:

pip install ndvi2gif

Usage

This is intend to be executed in a notebook and in tandem with a geemap Map object, so you could travel around the map and pick up your region of interest just by drawing a shape, and visualizing different dates and band combinations directly on the map. However, you could just run it in a command line and pass it a shapefile or a geojson as roi, and ask for the gif or for the geotiff rasters.

Please, see the example notebook

import geemap
from ndvi2gif import NdviSeasonality

#You could need a first login to sart with python Earth Enginelogin
ee.Initialize()

#Create the Map Object to choose he rois
Map = geemap.Map()
Map.add_basemap('Google Satellite')
Map

#Set the roi to last drawn feature
roi = Map.draw_last_feature

#Instance ndvi2gif
#Three different examples here to instantiate the class
myclass = NdviSeasonality(roi)
myclass2 = NdviSeasonality(roi, 2014, 2020, 'Landsat')
myclass3 = NdviSeasonality(roi, 2010, 2015, 'MODIS', key='median')

#Maybe you feel like playing with the Map and see different colour/season combination efore generate the gif
vizParams = {'bands': ['summer', 'autumn', 'winter'], 'min': 0, 'max': 0.7, 'gamma': [0.95, 1.1, 1]}
Map.addLayer(show, vizParams, 'mycropsfirstviz')

#Notice that you also can use the Earh Engine amazing analysis capabilities
wintermax = myclass.get_year_composite().select('winter').max()
median = myclass.get_year_composite().median()
Map.addLayer(wintermax, {'min': 0, 'max': 0.75}, 'winterMax')
Map.addLayer(median, {'min': 0.1, 'max': 0.8}, 'median')

#To get the gif, ust use the method.
myclass.get_gif()

#Last, you can export your yearly seasonal NDVI composites to your computer
myclass.get_export()

ToDo list

  • Add masking capablities based on NDVI values to show real color composite in the background. Is it that possible?
  • Add seasons dates as parameters that can be easily modified
  • Add a method to easily export multi-yearly composites

Contributions

Yes, please! git pulls will be welcome, even those related to my english grammar…

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for ndvi2gif, version 0.0.4
Filename, size File type Python version Upload date Hashes
Filename, size ndvi2gif-0.0.4.tar.gz (11.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page