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|Title:||A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction||Authors:||Sales, Márcio H.
de Bruin, Sytze
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
|Appears in Collections:||Άρθρα/Articles|
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