Jupyter Notebook - Vegetation Change
- Creators
- Killough, Brian
- Others:
- Open Data Cube Community
- CEOS SEO/AMA
Description
About
This notebook uses global Landsat-8 data from Google Earth Engine which has been indexed to work with the Open Data Cube. The selected data is used to calculate changes in the Normalized Difference Vegetation Index (NDVI) which is consistent with vegetation change. The algorithm identifies a "baseline" and "analysis" time period and then compares the spectral index in each of those time periods. Significant changes in NDVI (vegetation greenness) are coincident with land change, as long as the comparisons are done between similar time periods (seasons or years). Users of this algorithm should not accept the accuracy of the results but should conduct ground validation testing to assess accuracy. It is expected that this algorithm can be used to identify clusters of pixels that have experienced change and allow targeted investigation of those areas. In some cases the impacts may be negative (deforestation, mining, burning, drought) or positive (regrowth, improved soil moisture).
It should also be noted that the selection of the baseline and analysis time period is critical. First, the two time periods should be similar (season, year) so that the vegetation state can be compared in similar growing conditions. Second, the time periods should be contain sufficiently clear (non-cloudy) data. If the baseline or analysis mosaic (composite of images) is contaminated with clouds, it will impact the results. Users should review the Cloud Statistics notebook to understand the cloud conditions for a given region and time period.
This baseline notebook runs in about 5 minutes. The default region (0.1 degrees square) and time windows (one year each) uses about 10% of the allocated RAM memory. Selecting larger regions and time windows should be done carefully to avoid exceeding the system limits or having long run times.
Open Data Cube sandbox
You can execute this notebook in the Open Data Cube Sandbox. You can also run on your software promise environment.
For more information about the Open Data Cube Sandbox, please, check this link.