Lidar data provide critical information on the three-dimensional structure of forests and are often the primary information source used to develop gridded terrestrial data products, e.g., forest biomass and cover maps, for use in ecological studies. However, collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As a result, studies employing lidar data over large areas typically collect data via strip sampling; leaving large swaths of the forest domain unmeasured by the instrument. The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar, and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska’s Tanana Valley. The proposed modeling strategy facilitates pixel-level mapping of AGB density predictions across the entire spatial domain. Additionally, the coregionalization framework allows for statistically sound estimation of total AGB for arbitrary areal units within the study area, a key advance to support diverse management objectives in interior Alaska. This research focuses on appropriate characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent desired, ranging from pixel-level predictions of AGB density to estimates of AGB stocks for the full domain.
The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total AGB precision. Additionally, the inclusion of Landsat-derived forest cover as a model covariate further improved estimation precision in regions with lower lidar sampling intensity. Our findings also demonstrate that model-based approaches that do not explicitly account for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. On the other hand, in a geostatistical setting, residual spatial structure can be appropriately modeled within a Bayesian hierarchical framework to obtain statistically defensible measurements of uncertainty for AGB estimates.