AUTHOR=Temesgen Hailemariam , Mauro Francisco , Hudak Andrew T. , Frank Bryce , Monleon Vicente , Fekety Patrick , Palmer Marin , Bryant Timothy 

TITLE=Using Fay–Herriot Models and Variable Radius Plot Data to Develop a Stand-Level Inventory and Update a Prior Inventory in the Western Cascades, OR, United States

JOURNAL=Frontiers in Forests and Global Change

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2021.745916

DOI=10.3389/ffgc.2021.745916

ISSN=2624-893X

ABSTRACT=Stands are the primary unit for tactical and operational forest planning. Forest managers can use remote-sensing-based forest inventories to precisely estimate attributes of interest at the stand scale. However, remote-sensing-based inventories typically rely on models relating remote-sensing information to forest attributes for fixed area plots with accurate coordinates. The collection of that kind of ground data is expensive and time-consuming. Furthermore, remote-sensing-based inventories provide precise descriptions of the forest when the remote-sensing data were collected, but they inevitably become outdated. Fay-Herriot, FH, models can be used with ground information from variable radius plots even if the plot coordinates are unknown. Thus, they provide an efficient way to update old remote-sensing-based inventories or develop new ones when fixed radius plots are unavailable. In addition, FH models are well described in the small-area estimation literature and allow reporting estimation uncertainties, which is key to incorporating quality controls to remote-sensing inventories. 
We compared two scenarios developed in the Willamette National Forest, Oregon, USA, to produce stand-level estimates of above-ground biomass, AGB, and Volume, V for natural and managed stands. The first, Case 1, was developed using auxiliary data from a recent LIDAR acquisition. The second, Case 2, was developed to update an old remote-sensing-based inventory. Results showed that, for both case scenarios and both typologies of stands, FH models allowed for improvements in efficiency with respect to direct stand-level estimates obtained using only field data. Average improvements in efficiency in natural stands were 37.36% for AGB and 33.10% for Volume for FH models from Case 1 and 20.19% for AGB and 19.25 for V for Case 2. For managed stands, average improvements for Case 1 were 2.29% and 19.92% for AGB and V, respectively, and for Case 2, improvements were 15.55% for AGB and 16.05% for V.