Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping.

ABSTRACT: Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-coverage maps over large spatial domains. Given that LiDAR data and forest characteristics are often strongly correlated, it is possible to make use of the former to model, predict, and map forest variables over regions of interest. This entails dealing with the high-dimensional (~10²) spatially dependent LiDAR outcomes over a large number of locations (~10⁵-10⁶). With this in mind, we develop the Spatial Factor Nea

SUBMITTER: Taylor-Rodriguez D 

PROVIDER: S-EPMC7731981 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature