Data Cube Applications Library
The Data Cube Applications Library (DCAL) is a catalogue of Data Cube applications, with links to code, well documented Python Notebooks, sample Cube instances, webinars and videos, and other training materials to help users get up-and-running with specific Data Cube applications.
If you prefer to jump straight into code, the DCAL Python applications can be previewed here.
We are constantly adding new examples and hope you will contribute yours! Please check back often and sign up to our mailing list to see the latest additions.
A mosaic is a composite image created by combining the most appropriate pixels from a collection of source images. A common use case is to create cloud-free images for applications that are not time-dependent. There are a number of approaches.
Spectral Index algorithms calculate the relative magnitudes of wavelength components. The particular wavelength components used determine the product calculated.
Common Spectral Indices include NDVI, NDBI, NDWI, and products such as Fractional Cover.
Water Observations from Space (WOfS)
WOfS is an automated water mapping algorithm created by Geoscience Australia. Surface water is detected in satellite images and for each location the number of occurrences is summed through time. The result is a percentage value of the number of times water was observed at the location.
Normalized Difference Water Index (NDWI)
The Spectral Products page also contains several different spectral indices, including NDWI.
The land change notebook examines and classifies portions of significant change in spectral indices across a satellite time series. This product can be used for example to identify possible sites of deforestation or urbanization.
Cloud statistics are valuable information for performing analyses. For example, if there are extensive clouds during a season, it may significantly impact mosaic products or index values. Users may also want a way to find dates when there are few clouds, so that they may assess land features.