Stochastic, empirically‐informed model of landscape dynamics and its application to deforestation scenarios

Abstract

Land change including deforestation undermines the sustainability of the environment. Using data on 1992‐2015 pattern change in over 1.7 million meso‐scale landscapes worldwide we developed a stochastic model of long‐term landscape dynamics. The model suggests that observed heterogeneous landscapes are short‐lived stages in a transition between quasi‐stable homogeneous landscapes of different themes. As a case study we used Monte Carlo simulations based on our model to derive a probability distribution for evolutionary scenarios of landscapes that undergo a forest‐to‐agriculture transit, a prevalent element of deforestation. Results of simulations show that most likely and the fastest deforestation scenario is through the sequence of highly aggregated forest/agriculture mosaics with a decreasing share of the forest. Simulations also show that once forest share drops below 50% the remainder of the transit is rapid. This suggests that possible conservation policy is to protect meso‐scale tracts of land before the forest share drops below 50%.

Cite
Nowosad, J., Stepinski, T. F. (2019). Stochastic, empirically‐informed model of landscape dynamics and its application to deforestation scenarios. Geophysical Research Letters, DOI: 10.1029/2019GL085952
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