Constrained Functional Regression of National Forest Inventory Data Over Time Using Remote Sensing Observations

• Activity Number: 322 - Analyses in Ecology, Epidemiology, and Environmental Policy
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Avishek Chakraborty* and Giovanni Petris and Md Kamrul Hasan Khan and Barry T Wilson
• Companies: University of Arkansas and University of Arkansas and University of Arkansas and Northern Research Station, USDA Forest Service
• Keywords: Spatiotemporal model; Functional predictor; Binary regression; Live tree basal area

Abstract

The USDA Forest Service uses satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time throughout the United States. We specifically focus on a 230,400 hectare region in north-central Wisconsin between 2003-2012. The auxiliary data from the satellite imagery of this region are relatively dense in space and time, and can be used to learn how forest conditions changed over that decade. We use this imagery as a functional predictor in a two-component mixture model to capture the spatial variation in yearly average live tree basal area, an attribute of interest measured on field plots. We further modify the regression equation to accommodate a biophysical constraint on how plot-level live tree basal area can change from one year to the next. Findings from our analysis, represented with a series of maps, match known spatial and temporal patterns across the landscape.