GFW has some bespoke methods of encoding information into RGB+A channels, and decoding these data in a front-end application.
We should produce a tutorial explaining the steps taken to go from the original Hansen dataset to the final visualisation on GFW, so that 1) this knowledge can be spread effectively in the group, and 2) so that we can re-use and improve on these techniques in the future.

Create a notebook to demonstrate how to use Python to:

• Simple data access (both local and remote)
• extract metdatada
• obtain array slices
• Plotting of data
• use metadata info to identify closest points in space to desired locations (use of xray library?)
• Show how to use a widget to navigate through time dimension of a dataset?
• show how to save slices of data as a geotiff?

We often need to convert raster data from scientific datasets into lightweight versions meant for fast loading tiles. We should detail some standard methods of extracting and minifying (i.e. making the files lightweight).
For example, we can document how we would perform an ad hoc/per project assessment of what information is essential for a dataset to maintain, and what detail is not necessary, and a corresponding description of how to subsequently convert values into different formats (e.g. like mapping continuous temperature measurements into 256 integers), and saving the data as unsigned 16-bit integers, and finally compressing these data.