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A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction

Published on
August 21, 2017

A paper of Marcio Sales, Sytze de Bruin, Martin Herold, Phaedon Kyriakidis and Carlos Souza Jr: A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction, has been published in Spatial Statistics, Volume 21, Part A, August 2017, Pages 304-318.

doi.org/10.1016/j.spasta.2017.06.003

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.

Keywords: Land cover models; Deforestation; Spatiotemporal modeling; Hurdle models