This paper presents two modeling techniques which help to increase the realism of the computer generated images of terrain and vegetation. The issues of realism, as related to modeling of natural objects in the landscape, are concerned with the texture of natural terrain and the richness of vegetation growth patterns. The methods for modeling ground surfaces are basically divided into deterministic (i.e. polynomial) and stochastic methods. As opposed to polynomial methods, which provide for modeling of relatively smooth terrain surfaces, the stochastic methods, based on different classes of stochastic primitives, allow for the modeling of irregular (rough) terrain surfaces. This paper is particularly concerned with one such class of modeling primitives based on Iterated Function Systems (IFS). The paper also lays out the background theory and rule specification, together with software means, for the modeling of various vegetation growth patterns.