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- Title
SPATIAL FACTOR MODELS FOR HIGH-DIMENSIONAL AND LARGE SPATIAL DATA: AN APPLICATION IN FOREST VARIABLE MAPPING.
- Authors
Taylor-Rodriguez, Daniel; Finley, Andrew O.; Datta, Abhirup; Babcock, Chad; Andersen, Hans-Erik; Cook, Bruce D.; Morton, Douglas C.; Banerjee, Sudipto
- Abstract
Gathering information about forest variables is an expensive and arduous activity. Therefore, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next-generation collection initiatives for remotely sensed light detection and ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that Li-DAR data and forest characteristics are often strongly correlated, it is possible to use the former to model, predict, and map forest variables over regions of inter- est. This entails dealing with high-dimensional (~10²) spatially dependent LiDAR outcomes over a large number of locations (~105 - 106). With this in mind, we develop the spatial factor nearest neighbor Gaussian process (SF-NNGP) model, which we embed in a two-stage approach that connects the spatial structure found in LiDAR signals with forest variables. We provide a simulation experiment that demonstrates the inferential and predictive performance of the SF-NNGP, and use the two-stage modeling strategy to generate complete-coverage maps of the for- est variables, with associated uncertainty, over a large region of boreal forests in interior Alaska.
- Subjects
ALASKA; FOREST mapping; LIDAR; GAUSSIAN processes; TAIGAS
- Publication
Statistica Sinica, 2019, Vol 29, Issue 3, p1155
- ISSN
1017-0405
- Publication type
Article
- DOI
10.5705/ss.202018.0005