Data from: State-and-transition simulation models: a framework for forecasting landscape change

A wide range of spatially explicit simulation models have been developed
to forecast landscape dynamics, including models for projecting changes in
both vegetation and land use. While these models have generally been
developed as separate applications, each with a separate purpose and
audience, they share many common features. We present a general framework,
called a state-and-transition simulation model (STSM), which captures a
number of these common features, accompanied by a software product, called
ST-Sim, to build and run such models. The STSM method divides a landscape
into a set of discrete spatial units and simulates the discrete state of
each cell forward as a discrete-time-inhomogeneous stochastic process. The
method differs from a spatially interacting Markov chain in several
important ways, including the ability to add discrete counters such as age
and time-since-transition as state variables, to specify one-step
transition rates as either probabilities or target areas, and to represent
multiple types of transitions between pairs of states. We demonstrate the
STSM method using a model of land-use/land-cover (LULC) change for the
state of Hawai'i, USA. Processes represented in this example include
expansion/contraction of agricultural lands, urbanization, wildfire, shrub
encroachment into grassland and harvest of tree plantations; the model
also projects shifts in moisture zones due to climate change. Key model
output includes projections of the future spatial and temporal
distribution of LULC classes and moisture zones across the landscape over
the next 50 years. State-and-transition simulation models can be applied
to a wide range of landscapes, including questions of both land-use change
and vegetation dynamics. Because the method is inherently stochastic, it
is well suited for characterizing uncertainty in model projections. When
combined with the ST-Sim software, STSMs offer a simple yet powerful means
for developing a wide range of models of landscape dynamics.