For this map I created an interactive Google Earth Engine webmap for which I used a publicly available data set from the Google Earth Engine data catalogue containing worldwide data about the Terra Gross Primary Productivity.

I made this webmap to practice working with Google Earth Engine and therefore changed many different parameters in the code to make it to a new unique map. I changed the center of the map to Southeast Asia to center it to the region of interest and uploaded another layer containing a dataset with data about the Forest Cover at 10m spatial resolution which was collected in 2020. In order to be able to compare these two different datasets better, I modified the timeframe of data the map visualizes so that it shows the data collected in 2020. Moreover, I changed the colour of both datasets to different colour gradients so it fits the topic of the two layers and is easier for the viewer to identify the difference between the layers and compare them. To make the map easy to read, I coded my own legend and modified the colours, spacing and labels to make it suit the topics of the two datasets. All these changes were achieved by working with and changing the code to customize the map I received from the publicly available dataset in order to make it a new map with a different topic and communication aim.

I chose to create a map with these two indicators as forests are an important contributor to the Gross Primary Productivity, which is important for carbon sequestration and ecosystem services in general, and enables informed decision making regarding land use planning.