Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5119004 | Spatial Statistics | 2017 | 15 Pages |
â¢We have built a novel predictive deforestation model using a spatiotemporal hurdle approach.â¢The model was successful in predicting deforestation at 1 km spatial resolution.â¢The model provides an option for land cover modeling applications.
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.