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Title: A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction
Authors: Sales, Márcio H. 
de Bruin, Sytze 
Herold, M. 
Kyriakidis, Phaedon 
Souza, Carlos Moreira 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Land cover models;Deforestation;Spatiotemporal modeling;Hurdle models
Issue Date: Aug-2017
Source: Spatial Statistics, 2017, vol. 21, pp. 304-318
Volume: 21
Start page: 304
End page: 318
Journal: Spatial Statistics 
Abstract: This paper introduces and tests a geostatistical spatiotemporal hurdle approach for predicting the spatial distribution of future deforestation (one to three years ahead in time). The method accounts for neighborhood effects by modeling the auto-correlation of occurrence and intensity of deforestation, using a spatiotemporal geostatistical specification. Deforestation observations are modeled as a function of pertinent control variables, such as distance to roads and protected areas, and the model accounts for space–time autocorrelated residuals with non-stationary variance. Applied to the Brazilian Amazon, the model predicted the locations of new deforestation events with over 90% agreement. In addition, 100% of the deforestation intensity values were contained in the model’s confidence bounds. The features of the model and validation results qualify the model as a strong candidate for short-term deforestation modeling.
ISSN: 2211-6753
DOI: 10.1016/j.spasta.2017.06.003
Rights: © Elsevier
Type: Article
Affiliation : Wageningen University 
Cyprus University of Technology 
Instituto do Homem e do Meio Ambiente da Amazοnia 
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