@Article{SilvaFonsKörtEsca:2020:SpBaNe,
author = "Silva, Alexsandro C{\^a}ndido de Oliveira and Fonseca, Leila
Maria Garcia and K{\"o}rting, Thales Sehn and Escada, Maria
Isabel Sobral",
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)}",
title = "A spatio-temporal Bayesian Network approach for deforestation
prediction in an Amazon rainforest expansion frontier",
journal = "Spatial Statistics",
year = "2020",
volume = "35",
pages = "e100393",
month = "Mar.",
keywords = "Bayesian Networks, Spatio-temporal modeling, Environmental
modeling, Deforestation, Brazilian Amazon forest.",
abstract = "In the last decade, Brazil has successfully managed to reduce
deforestation in the Amazon forest. However, continued increases
in annual deforestation rates call for environmental modeling to
support short-term decision-making. This paper presents the
functioning of a stepwise spatio-temporal Bayesian Network
approach for spatially explicit analysis of deforestation risk
based on observation data. The study area comprises a
deforestation expansion frontier located in the southwest of
Par{\'a} state, Brazil. The proposed approach has been successful
in estimating deforestation risk over the years. Among the
selected variables to compose the Bayesian Network model, distance
from hot spots and distance from degraded areas present the
highest contribution, while protected areas variable present a
significant mitigation effect on the phenomenon. Accuracy
assessment indices corroborate the agreement between deforestation
events and predictions.",
doi = "10.1016/j.spasta.2019.100393",
url = "http://dx.doi.org/10.1016/j.spasta.2019.100393",
issn = "2211-6753",
language = "en",
targetfile = "silva_spatio.pdf",
urlaccessdate = "27 abr. 2024"
}