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Gibbsian T-tessellation model for agricultural landscape characterization

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02905984
Contributor : Katarzyna Adamczyk-Chauvat <>
Submitted on : Friday, July 31, 2020 - 11:04:46 AM
Last modification on : Saturday, August 8, 2020 - 3:11:02 AM

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  • HAL Id : hal-02905984, version 1
  • ARXIV : 2007.16094

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Katarzyna Adamczyk-Chauvat, Mouna Kassa, Kiên Kiêu, Julien Papaïx, Radu S. Stoica. Gibbsian T-tessellation model for agricultural landscape characterization. 2020. ⟨hal-02905984⟩

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