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Activity Number: 409 - Bayesian Space-Time Modeling
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #304624
Title: Bayesian Spatio-Temporal Models for Map Reconstruction and Forest Inventory Prediction
Author(s): Giovanni Petris* and Avishek Chakraborty and Kamrul Khan and Ty Wilson
Companies: Univ of Arkansas and University of Arkansas and University of Arkansas and USDA Forest Service
Keywords: Spatio-temporal models; Remote sensing; CAR prior; Bayesian

The USDA Forest Service aims to use satellite imagery for monitoring and predicting changes in forest conditions over time across large geographic regions within the country. The auxiliary data collected from satellite are relatively dense in space and time and can be used to efficiently predict how the forest condition changes over time. However, the auxiliary data contain a huge proportion of missing values at every location. We develop a spatio-temporal model to reconstruct these missing values from posterior predictive distributions. The model consists of a temporal fixed effect based on periodic patterns, a spatio-temporal random effect based on a conditional autoregressive (CAR) prior and a temporal random effect based on a AR(1) prior. Once we reconstruct the full spatio-temporal map, we use it to model the presence/absence of forest and the amount of basal area across the region. These models are formulated using functional regression. Horseshoe regularization is performed to identify important auxiliary variables. Restriction in sudden increase in the basal area is also incorporated in the model.

Authors who are presenting talks have a * after their name.

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