کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5119004 | 1378193 | 2017 | 15 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction](/preview/png/5119004.png)
- We have built a novel predictive deforestation model using a spatiotemporal hurdle approach.
- The model was successful in predicting deforestation at 1 km spatial resolution.
- 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.
Journal: Spatial Statistics - Volume 21, Part A, August 2017, Pages 304-318