| 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. |
| Genre: | Article |
| ProdID: | 70041615 |
| Citation Author: | Finley, Andrew O.; Banerjee, Sudipto ; Cook, Bruce D.; Bradford, John B. |
| Citation Contributing Office: | Southwest Biological Science Center |
| Citation Datum: | |
| Citation Day: | |
| Citation Edition: | |
| Citation Editor: | |
| Citation End Page: | 160 |
| Citation Issue: | |
| Citation Keywords: | |
| Citation Language: | English |
| Citation Larger Work Title: | International Journal of Applied Earth Observation and Geoinformation |
| Citation LatN: | 47.46 |
| Citation LatS: | 42.97 |
| Citation LonE: | -66.95 |
| Citation LonW: | -71.08 |
| Citation Month: | JUN |
| Citation No Pagination: | |
| Citation Number Of Pages: | |
| Citation Online Only Flag: | |
| Citation Phsyical Description: | 14 p. |
| Citation Projection: | |
| Citation Public Comments: | |
| Citation Publisher: | Elsevier |
| Citation Series: | |
| Citation Series Code: | |
| Citation Series Number: | |
| Citation Search Results Text: | Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets; 2013; Article; Journal; International Journal of Applied Earth Observation and Geoinformation; Finley, Andrew O.; Banerjee, Sudipto ; Cook, Bruce D.; Bradford, John B. |
| Citation Start Page: | 147 |
| Citation Volume: | 22 |
| Citation Year: | 2013 |
| Type: | citation/reference |
| Text: | Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets; 2013; Article; Journal; International Journal of Applied Earth Observation and Geoinformation; Finley, Andrew O.; Banerjee, Sudipto ; Cook, Bruce D.; Bradford, John B. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg |
| URL (DIGITAL OBJECT IDENTIFIER): | http://dx.doi.org/10.1016/j.jag.2012.04.007 |
| Date Other: | Sat, 8 Dec 2012 00:00 -0600 |
| Publisher: | Elsevier |
