@InCollection{XimenesAlmAmaEscAgu:2011:SpDyMo,
author = "Ximenes, Arimat{\'e}a de Carvalho and Almeida, Cl{\'a}udia Maria
de and Amaral, Silvana and Escada, Maria Isabel Sobral and Aguiar,
Ana Paula Dutra de",
title = "Spatial Dynamic Modeling of Deforestation in the Amazon",
booktitle = "Cellular Automata - Simplicity Behind Complexity",
publisher = "InTech",
year = "2011",
editor = "Salcido, Alejandro",
pages = "xx",
address = "Vienna",
keywords = "Behind Complexity.",
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 S{\~a}o F{\'e}lix 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 drove the simulation model of the present
work, and statistical validation tests were then conducted for the
generated simulations (from 1997 to 2000), by means of multiple
resolution fitting methods. This modelling experiment demonstrated
the suitability of the adopted model to simulate processes of
forest conversion, unravelling the relationships between site
attributes and deforestation in the area under analysis.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
isbn = "978-953-307-230-2",
label = "lattes: 7327236294706424 1 XimenesAlmAmaEscAgu:2011:SpDyMo",
language = "en",
targetfile = "
InTech-Spatial_dynamic_modelling_of_deforestation_in_the_amazon.pdf",
url = "http://www.intechopen.com/articles/show/title/spatial-dynamic-modelling-of-deforestation-in-the-amazon",
urlaccessdate = "20 maio 2024"
}