Urban environments are heterogeneous at relatively small scales and composed of multiple land cover types but are dominated by vegetation, impervious surface, and soil (V-I-S). Land cover indices such as the Biophysical Composition Index (BCI), based on the tasseled cap transformation, attempt to capture the spatial pattern of these three broad classes of urban land cover. Here I present Python examples for applying the tasseled cap transformation and for calculating the BCI.
I experimented with Bayesian networks for land cover classification in the Detroit metro area using census data as predictors. An example of using Bayesian networks for land cover classification in R is presented with code samples.