Leveraging forest inventory data to estimate forest carbon density status and trends for small areas

Music City Center 

National forest inventory (NFI) data are often costly to collect, which inhibits efforts to estimate parameters of interest for small spatial, temporal, or biophysical domains. Traditionally, design-based estimators of forest parameters are used to estimate status of forest metrics of interest, but are unreliable for small areas where data are sparse. Further, direct estimates are often unavailable when sample sizes are especially small. Direct estimate missingness precludes use of traditional small area estimation (SAE) estimators such as Fay–Herriot type models. Here we propose a spatio-temporal SAE model that efficiently uses sparse NFI data to estimate status and trends for forest parameters. The proposed model bypasses the use of direct estimates, and instead uses sampling unit measurements along with auxiliary data including remotely sensed percent tree canopy cover. We provide an analysis of real forest carbon NFI data from the United States Forest Service Forest Inventory and Analysis program over 14 years across the contiguous US, and conduct a simulation study to assess bias, coverage, and model accuracy.

National Forest Inventory

Small Area Estimation

Bayesian

Spatio-temporal 

Section on Statistics and the Environment