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

Marcio Ribeiro Sales*, Sytze De Bruin, Martin Herold, Phaedon Kyriakidis, Carlos Souza

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)304-318
JournalSpatial Statistics
Issue numberpart A
Publication statusPublished - 2017


  • Deforestation
  • Hurdle models
  • Land cover models
  • Spatiotemporal modeling


Dive into the research topics of 'A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction'. Together they form a unique fingerprint.

Cite this