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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provide precise descriptions of the forest when the remote-sensing data were collected, but they inevitably become outdated as the forest evolves. 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, OR, United States, 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 FH models allowed for improvements in efficiency with respect to direct stand-level estimates obtained using only field data for both case scenarios and both typologies of stands. 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. |
