I'm building a random forest model that is designed to predict tree canopy cover. I'm using an R software environment and satellite remote sensing data as my predictors.

I'm okay with under or over estimating the tree canopy % where there are actually trees, but it's extremely important that my model can assess a tree canopy value of 0 when it is 0. I don't want predictions that give me a 10% tree canopy cover on a highway.

How can I tune my model to place a higher emphasis on 0 values?