Skip to Main Content
LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficientsAuthor(s): Chad Babcock; Andrew O. Finley; John B. Bradford; Randy Kolka; Richard Birdsey; Michael G. Ryan
Source: Remote Sensing of Environment. 169: 113-127.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
View PDF (3.0 MB)
DescriptionMany studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial randomeffects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, firstname.lastname@example.org if you notice any errors which make this publication unusable.
CitationBabcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall; Birdsey, Richard; Ryan, Michael G. 2015. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients. Remote Sensing of Environment. 169: 113-127.
KeywordsBayesian hierarchical models, Markov chain Monte Carlo, Gaussian process, Geospatial, LiDAR, Forest biomass
- Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data
- Multilevel nonlinear mixed-effects models for the modeling of earlywood and latewood microfibril angle
- Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring
XML: View XML