Spatially constrained forest cover dynamics using Markovian random processes

Essential to analyses of forest-cover change is application of geospatial empirical or semi-empirical models of transition potentials based on the likelihood that forest land would change to non-forested land or vice versa depending on prevailing conditions of land-use change. Modeling land-cover as a function of land-use aids in understanding pertinent land-cover dynamics. This can enable forecasting of ramifications of current conversion processes on land designated for agriculture or development. National Agricultural Statistics Service (NASS) grid data published by USDA for years 1997–2002 were used as preliminary inputs. Two prevalent transition probabilities were derived: probability of a pixel changing (a) from forested to non-forested, Pfnf and (b) from non-forested to forested, Pnff. These probabilities were determined for the years (i) 1999–2000, (ii) 2000–2001, (iii) 1999–2001, and (iv) 1999–2009 using decade stratification. The maximal transition probability ranges for forest to non-forest transition were higher for 1999–2000 and 1999–2001 transiting periods compared to 2000–2001. The maximal transition probability ranges for non-forest to forest transition were lower for 1999–2000 transiting period compared to 2000–2001, and 1999–2001 transiting periods. The analysis provided a glimpse on areas deemed prone to forest conversion and those that would immensely benefit from federally funded programs.

► Regional maximum likelihood estimates can be derived using reduced and categorized Markovian chains of first order.
► Markovian simulations may illustrate potential interactions between thresholds in landscape patterns.
► Coupled-modeling approaches offer inchoate mechanistic springboards for decision-making.
► Cellular-Automata Markovian processes and spatial analyses are vital for Forest Stewardship Program implementation.