Sentinel-2 toolbox

Work with sentinel-2 images easily. If you know how to download Sentinel-2 Multi-Spectral-Images (or Electro-Optical), but you don't know how to work with them in Python, I have it all implemented here for you.

Content

• Functions
• Illustration of functions
• Demonstration on usage

Functions:

• load_bands()
• contours()
• contour2poly()
• ndvi_index()
• rvi_index()
• savi_index()
• evi_index()
• rgb_img()

Demonstration on usage:

images = load_bands('F:\\S2_billeder\\..\\IMG_DATA',bands=['B03','B04','B05','B01'])

This function will load the bands of your chosing into python. You could load them all, but it is recommended to only load the bands that are needed for later analyses, as they take a lot of memory (i.e. calcuating a simple NDVI index.)

contours, contours_types = contours(ndvi_image,mode='ndvi',method='skimage')

This function finds contours in a false colours image. This is useful when working with e.g. Index values(NDVI etc.).

poly =  contour2poly(contours,tolerance=2)

This function creates geo-references polygons. Often, when working with e.g. satellite images, the results must be displyed in a GIS software, e.g. QGIS. The results from an analysis must consequently be turned into polygons that can be exported into e.g. .shape files. Thus, calculate an index product, and thereafter find the contours for different resut, and lastly find the polygons. This can then be used in QGIS.

INDEX PRODUCTS

Index products are simple products calculated using band-operations.

ndvi_index = ndvi(B04, B08)

Normalized difference vegetation index (NDVI) from Sentinel-2 images. A simple index displaying how "healty" the vegetation is by indirectly measuring the amount of chlorophyll. This is done by using the load_bands() function to return band 04 and 08, and thereafter using the ndvi function.

Getting started

Download or clone.

Installing

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Authors

Kristian Aalling Sørensen