Date of Graduation

12-2018

Document Type

Thesis

Degree Name

Master of Science in Statistics and Analytics (MS)

Degree Level

Graduate

Department

Statistics and Analytics

Advisor/Mentor

Chakraborty, Avishek A.

Committee Member

Petris, Giovanni G.

Second Committee Member

Tipton, John R.

Keywords

Conditional autoregressive prior; Landsat time series; Spatiotemporal model

Abstract

The USDA Forest Service aims to use satellite imagery for monitoring and predicting changes in forest conditions over time within the country. We specifically focus on a 230, 400 hectares region in north-central Wisconsin between 2003 - 2012. The auxiliary data collected from the satellite imagery of this region are relatively dense in space and time and can be used to efficiently predict how the forest condition changed over that decade. However, these records have a significant proportion of missing values due to weather conditions and system failures. To fill in these missing values, we build spaciotemporal models based on fixed effect periodic patterns, spatial random effects with conditional autoregressive prior and a first-order autoregressive temporal effect. Multiple validation and comparison diagnostics are run to identify the best performing model for each of the auxiliary variables as well as for basal area. Findings from our analysis are represented with a series of maps followed by a discussion of their agreement with known spatial patterns across the landscape.

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