Lidar provides critical information on the three-dimensional structure of forests. However, collecting wall-to-wall laser altimetry data at regional and global scales is cost prohibitive. As a result, studies employing lidar for large area estimation typically collect data via strip sampling, leaving large swaths of the forest unmeasured by the instrument. The goal of this research was to develop and examine the performance of a coregionalization modeling approach for combining field measurements, strip samples of airborne lidar and Landsat-based remote sensing products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates mapping of AGB density across the domain. Additionally, the coregionalization framework allows for 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 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, ranging from point-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 (mean) AGB precision. Additionally, including a Landsat-derived forest cover covariate further improved precision in regions with lower lidar sampling intensity. Findings also demonstrate that model-based approaches not explicitly accounting for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. The inferential capabilities of AGB posterior predictive distribution (PPD) products extend beyond simply mapping AGB density. We show how PPD products can provide insight regarding drivers of AGB heterogeneity in boreal forests, including permafrost and fire, highlighting the range of potential applications for Bayesian geostatistical methods to integrate field, airborne and satellite data.
Babcock, C., Finley, A. O., Andersen, H. E., Pattison, R., Cook, B. D., Morton, D. C., … Næsset, E. (2018). Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations. Remote Sensing of Environment, 212, 212–230. https://doi.org/10.1016/j.rse.2018.04.044