satellite-images-nso 1.1.9

Satellie extraction and cutter

Introduction

This python code is intended to automate/make easier the data extraction and cutting of satellite data from the netherlands space office (NSO). NSO provides free satellite images from the Netherlands, a downside however is that the NSO provides a very large region and as such a very large data file. This leads to a unnecessary large amount of data, if you only want to study a smaller specific region.

This python code cuts a selected region out of the original satellite image, provided that the selected region is smaller than the original file. And then saves this cutout thus reducing the unnecessary saved data. While also calculating the Normalized difference vegetation index (NVDI, used in crop analysis) of the cutout region. We are working on extracting more variables on satellite images.

This image gives a illustration:

Getting Started

0. Get a NSO account, register at https://satellietdataportaal.nl/register.php
1. First get a GeoJSON file of the region you want to cut. Geojson.io can you help you with that.
2. Make a instance of nso_geojsonregion with instance of the geojson region you have, where you want to store the cropped files and the NSO account based on step 0.
3. Retrieve download links for the specific region you want to have.
4. Download the found links.

Example code.

# This the way the import nso.
import satellite_images_nso.api.nso_georegion as nso
path_geojson = "/src/example/example.geojson"
# The first parameter is the path to the geojson, the second the map where the cropped satellite data will be installed
georegion = nso.nso_georegion(path_geojson,"/src/output/",\
                              YOUR_USER_NAME_HERE,\
                             YOUR_PASSWORD_HERE)

# This method fetches all the download links to all the satelliet images which contain region in the geojson.
links = georegion.retrieve_download_links()

# Downloads a satelliet image from the NSO, make a crop out of it so it fits the geojson region and calculate the NVDI index.
# The output will stored in the designated output folder.
georegion.execute_link(links[0])


# The sat_manipulator gives other handy transmations on satellite data.
import satellite_images_nso.api.sat_manipulator as sat_manipulator

# This function reads a .tif file, which is a format the satellite data is stored in,  and converts it to a pixel based geopandas dataframe.
# For machine learning purposes.
path_to_vector = "path/to/folder/*.tif"
geo_df_pixel = sat_manipulator.tranform_vector_to_pixel_gpdf(path_to_vector)

Installation.

Install this package with: pip install satellite_images_nso

Be sure you've installed GDAL already on your computer. Other python dependencies will install automatically (Fiona>=1.8.13, GDAL>=3.0.4, geopandas>=0.7.0, rasterio>=1.1.3 Shapely>=1.7.0)

Install GDAL on Windows

If you are a Windows user you have to install the GDAL dependency yourself via a wheels.

Instead install these wheels with pip install XXX.XX.XX.whl.

Go to https://www.lfd.uci.edu/~gohlke/pythonlibs/ for the wheels of these depencies:

Depencencies are : "Fiona>=1.8.13", "GDAL>=3.0.4", "geopandas>=0.7.0","rasterio>=1.1.3","Shapely>=1.7.0"

Install GDAL on MacOS

Install GDAL by using Brew:
brew install GDAL

Run as a docker container

docker run -it --entrypoint bash dockerhubpzh/satellite_images_nso_docker

See: https://hub.docker.com/r/dockerhubpzh/satellite_images_nso_docker

Local development

Run rebuild.bat to build and install package on local computer.

Author

Michael de Winter

Daniel Overdevest

Yilong Wen