Hello All.
I am working on a theoretical habitat suitability model and am seeking to not treat the landscape as static and want to be able to introduce some stochastic values into the analysis recognizing that animals do not really differentiate lands based on values. My hope is that I can create a number of iterations (n = 10 right now) of the model based on reclassified values (0-5), feed these iterations into individual weighted sum tools (n = 10), and then create a final suitability raster layer based on areas of agreement between the 10 suitability models.
I am trying to figure out if there is a way to reclassify raster files using an iterative process where I can randomly assign a reclassification value to the cells and then feed all of these into weighted sum tools.
Thanks for any suggestions and have a great one.
