Phase 1
The project made use of the Astro Pi NoIR camera to take photos of the earth surface. Location and time of each photo were retrieved using the "orbit" Python package and saved in the image itself as metadata with the help of the “exif” Python library. Both “orbit” and “exif” are supported by the Flight OS.
The orbit package was also used to decide whether to take photos depending on the position of the ISS (take photos only if the ISS was in sunlight, to save memory).
All the analysis was done during phase 4.
The first type of analysis was to determine for each image if it was taken over water or clouds to discard the useless ones. This was accomplished trough a NDVI based classification of pixels knowing that water has a lower NDVI value than soil. We also used a threshold based algorithm to identify images taken over clouds. Another threshold based script was used to discard darker picures.
The selected data was then used, combined with geographical coordinates, to determine if the places photographed are affected by desertification.
More precisely to distinguish drought versus non-drought areas we will write a Python script that performed the following tasks:
- Calculate the average NDVI value for each place photographed.
- Calculate the average NDVI value of the same places over the past years using data from Google Earth Engine.
- Calculate the Vegetation Condition Index from Long-term Maximum and Long-term Minimum of the NDVI collected data.
- The resulting percentage is situated between the extreme values (minimum and maximum) in the previous years. Lower and higher values therefore indicate bad and good vegetation state conditions, respectively.
- Determine if the area is affected by desertification using a threshold of 40%.
AstroPI NoIR camera