Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets
International Journal of Applied Earth Observation and Geoinformation
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Abstract
In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets |
| Series title | International Journal of Applied Earth Observation and Geoinformation |
| DOI | 10.1016/j.jag.2012.04.007 |
| Volume | 22 |
| Year Published | 2013 |
| Language | English |
| Publisher | Elsevier |
| Publisher location | Amsterdam, Netherlands |
| Contributing office(s) | Southwest Biological Science Center |
| Description | 14 p. |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | International Journal of Applied Earth Observation and Geoinformation |
| First page | 147 |
| Last page | 160 |
| Country | United States |
| State | Maine |
| Other Geospatial | Penobscot Experimental Forest |
| Google Analytic Metrics | Metrics page |