Model Development

We used the random forest classifier (Breiman 2001) implemented in Google Earth Engine (GEE) to predict each landcover class in our taxonomy from the covariates at all training data locations. We trained 10 replicate models per landcover class in a k-fold approach (Fushiki 2011) with a unique 'fold' of 1/10th of the data withheld for validation as described above in the training data section. The model predicted the probability that the observation belonged to the class of interest rather than any other class.

In addition to assessing the probability that a pixel belonged to each landcover class in our taxonomy, we also created a discrete landcover classification based on the landcover class with the highest probability in each pixel. We modified this discrete classification by assigning all pixels that coincided with the road classes of 'freeway' or 'primary road' a class value of 19 to indicate the presence of a major road.

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