Understanding InVEST's approach to ES trade-offs

Hi, I’m using InVEST to study the correlation between land cover arrangements and ES supply. I’m focussing on 4 ES types, which will be weighted according to ES demand in this particular location. I will be running hundreds of land cover scenarios, using a limited set of LULC types represented by randomly generated contiguous shapefiles, arranged within a fixed site area.
I’m interested in how I can use InVEST for ES trade-off analysis. This will help determine what landscape arrangements are best in meeting the specific ES demand of the locality.
Is it a matter of doing this analysis ‘manually’ i.e. visually comparing ES performance calculated by InVEST over a range of scenarios? Or is there an algorithm in place that could assist with this? I suspect there isn’t such an algorithm, but I would value any feedback on how other InVEST users have tackled this type of exercise.

Many thanks

HI @Minton71 -

I’m not sure if this is partly redundant or even relevant to your workflow, but have you looked at the NatCap tool ROOT? It’s a bit complicated (maybe even more so if you’re running hundreds of scenarios), but is designed to help with optimization and tradeoff analysis using InVEST results.

I haven’t worked with so many scenarios in a project, but if you have a lot of runs like that, you could consider taking the top 10% (or whatever) of service-provision pixels in each scenario run, and see where they overlap. Those places could be robust to providing service under different landscape configurations.

Otherwise, I’d love to hear from others who have done this kind of analysis.

~ Stacie