Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/10291
Title: | A spatiotemporal geostatistical hurdle model approach for short-term deforestation prediction |
Authors: | Sales, Márcio H. de Bruin, Sytze Herold, M. Kyriakidis, Phaedon 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. |
URI: | https://hdl.handle.net/20.500.14279/10291 |
ISSN: | 22116753 |
DOI: | 10.1016/j.spasta.2017.06.003 |
Rights: | © Elsevier |
Type: | Article |
Affiliation : | Wageningen University Cyprus University of Technology Instituto do Homem e Meio Ambiente da Amazônia |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
Sorry the service is unavailable at the moment. Please try again later.
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.