In a previous blog post I describe how to subset MODIS hdf data. However, this was a rather simple example. Today, a graduate student emailed me to help her out with a subsetting problem she had when running the code, or better the lack of an option to extract a region of interest (rather than point data) in the previous example I gave.
It is surprising that instead of questioning how we assess academic work we question the license under which it will be distributed.
In business management the Pareto principle, or the law of the vital few, translates into the notion that 20% of the clients bring in 80% of the sales. I argue that in science the same principle applies. Here, focussing 20% of your time on necessary projects will translate in the bulk of your output.
In much of my work I use geospatial data (either vector or raster maps). However, visualizing this data easily for people unfamiliar with geographic information system (GIS) toolsets is often difficult as end users care about the result (a nice map) not learning visualization tools. In short, to communicate your work you need to present maps in an appealing way.
Within the context of the CLIMO COST action there was a need for a mountain forest map (EU wide if not global), as defined by FAO rules. These rules specify that forests above 300 m and with a considerable slope are “mountain forest”. I put together a Google Earth Engine worked example to generate this data.