English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The goal of this research was to develop and examine the performance of ageostatistical coregionalization modeling approach for combining fieldinventory measurements, strip samples of airborne lidar and Landsat-basedremote sensing data products to predict aboveground biomass (AGB) in interiorAlaska's Tanana Valley. The proposed modeling strategy facilitates pixel-levelmapping of AGB density predictions across the entire spatial domain.Additionally, the coregionalization framework allows for statistically soundestimation of total AGB for arbitrary areal units within the study area---a keyadvance to support diverse management objectives in interior Alaska. Thisresearch focuses on appropriate characterization of prediction uncertainty inthe form of posterior predictive coverage intervals and standard deviations.Using the framework detailed here, it is possible to quantify estimationuncertainty for any spatial extent, ranging from pixel-level predictions of AGBdensity to estimates of AGB stocks for the full domain. The lidar-informedcoregionalization models consistently outperformed their counterpart lidar-freemodels in terms of point-level predictive performance and total AGB precision.Additionally, the inclusion of Landsat-derived forest cover as a covariatefurther improved estimation precision in regions with lower lidar samplingintensity. Our findings also demonstrate that model-based approaches that donot explicitly account for residual spatial dependence can grosslyunderestimate uncertainty, resulting in falsely precise estimates of AGB. Onthe other hand, in a geostatistical setting, residual spatial structure can bemodeled within a Bayesian hierarchical framework to obtain statisticallydefensible assessments of uncertainty for AGB estimates.

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations