  A new class of planar tessellations, named T-tessellations, was introduced in
([10]). A model was proposed to be considered as a completely random
T-tessellation model (CRTT) and its Gibbsian variants were discussed. A general
simulation algorithm of Metropolis-Hastings-Green type was derived for model
simulation, involving three local transformations of T-tessellations. The
current paper focuses on statistical inference for Gibbs models of
T-tessellations. Statistical methods originated from point pattern analysis are
implemented on the example of three agricultural landscapes approximated by
T-tessellations. The choice of model statistics is guided by their capacity to
highlight the differences between the landscape patterns. Model parameters are
estimated by Monte Carlo Maximum Likelihood method, yielding a baseline for
landscapes comparison. In the last part of the paper a global envelope test
based on the empty-space function is proposed for assessing the goodness-of-fit
of the model.
