activity
20142023
most citedRemote sensing to reduce the effects of spatial autocorrelation on design-based inference for forest inventory using systematic samples

2 citations · 2 across the 6 of their papers we have counts for

collaborators

8 papers

stat.AP2023

Models to support forest inventory and small area estimation using sparsely sampled LiDAR: A case study involving G-LiHT LiDAR in Tanana, Alaska

Andrew O. Finley, Hans-Erik Andersen, Chad Babcock +3

A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory pl…

stat.AP2022

Quantifying and correcting geolocation error in spaceborne LiDAR forest canopy observations using high spatial accuracy ALS: A Bayesian model approach

Elliot S. Shannon, Andrew O. Finley, Daniel J. Hayes +4

Geolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with…

stat.AP2020

A Bayesian hierarchical model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images

Chad Babcock, Andrew O. Finley, Nathaniel Looker

We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel…

stat.AP2018★ 2 cited

Remote sensing to reduce the effects of spatial autocorrelation on design-based inference for forest inventory using systematic samples

Chad Babcock, Andrew O. Finley, Timothy G. Gregoire +1

Systematic sampling is often used to select plot locations for forest inventory estimation. However, it is not possible to derive a design-unbiased variance estimator for a systema…

stat.AP2018

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

Daniel Taylor-Rodriguez, Andrew O. Finley, Abhirup Datta +5

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…

stat.AP2017

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

Chad Babcock, Andrew O. Finley, Hans-Erik Andersen +9

The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip sampl…

stat.AP2016

Joint hierarchical models for sparsely sampled high-dimensional LiDAR and forest variables

Andrew O. Finley, Sudipto Banerjee, Yuzhen Zhou +2

Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spa…

stat.AP2014

Dynamic spatial regression models for space-varying forest stand tables

Andrew O. Finley, Sudipto Banerjee, Aaron R. Weiskittel +2

Many forest management planning decisions are based on information about the number of trees by species and diameter per unit area. This information is commonly summarized in a sta…