Spatial Dynamic Modelling of Deforestation in the Amazon

Abstract

New GIS technologies have been employed to support public policies and actions towards environmental conservation, aiming to preserve biodiversity and mitigate the undesirable side-effects of human activities. The spatio-temporal simulation of systems dynamics is an example of such new technologies and helps scientists and decision-makers to understand the driving forces lying behind processes of change in environmental systems. In assessing how systems evolve, it is possible to figure out different scenarios, given by diverse socioeconomical, political and environmental conditions (Soares-Filho et al., 2001), and hence, anticipate the occurrence of certain events, like land cover and land use change, including deforestation. According to Openshaw (2000), computer simulation models provide qualitative and quantitative information on complex natural phenomena. In this sense, spatial dynamic models may be defined as mathematical representations of real-world processes or phenomena, in which the state of a given place on the Earth surface changes in response to changes in its driving forces (Burrough, 1998). Spatial dynamic models are commonly founded on the paradigm of cellular automata (CA). Wolfram (1983) defines CA as “[...] mathematical idealisations of physical systems in which space and time are discrete, and physical quantities take on a finite set of discrete values. A cellular automaton consists of a regular uniform lattice (or ‘array’), usually infinite in extent, with a discrete variable at each site (‘cell’). [...] A cellular automaton evolves in discrete time steps, with the value of the variable at the site being affected by the values of variables at sites in its ‘neighbourhood’ on the previous time step. The neighbourhood of a site is typically taken to be the site itself and all immediately adjacent sites. The variables at each site are updated simultaneously (‘synchronously’), based on the values of the variables in their neighbourhood at the preceding time step, and according to a definite set of ‘local rules’.” (Wolfram, 1983, p. 603). This work applies a CA model – Dinamica EGO – to simulate deforestation processes in a region called Sao Felix do Xingu, located in east-central Amazon. EGO consists in an environment that embodies neighbourhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables