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Stochastic, Empirically Informed Model of Landscape Dynamics and Its Application to Deforestation Scenarios
Author(s) -
Nowosad J.,
Stepinski T. F.
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl085952
Subject(s) - deforestation (computer science) , mesoscale meteorology , sustainability , agriculture , land use, land use change and forestry , geography , environmental science , ecology , computer science , meteorology , archaeology , biology , programming language
Land change including deforestation undermines the sustainability of the environment. Using data on 1992–2015 pattern change in over 1.7 million mesoscale 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 mesoscale tracts of land before the forest share drops below 50%.