Chad Babcock

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

Andrew O. Finley, Douglas C. Morton, Sudipto Banerjee, 2018, Statistica Sinica.

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

Andrew O. Finley, Sudipto Banerjee, Chad Babcock, 2016, 1603.07409.

Harnessing the Temporal Dimension to Improve Object-Based Image Analysis Classification of Wetlands

L. Monika Moskal, Chad Babcock, Meghan Halabisky, 2018, Remote. Sens..

Multivariate Spatial Regression Models for Predicting Individual Tree Structure Variables Using LiDAR Data

Andrew O. Finley, Aaron Weiskittel, Chad Babcock, 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Dynamic spatial regression models for space‐varying forest stand tables

Andrew O. Finley, Sudipto Banerjee, Aaron R. Weiskittel, 2014, 1411.0599.

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

Andrew O. Finley, Chad Babcock, Nathaniel Looker, 2021 .

Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

Douglas C. Morton, Hans-Erik Andersen, L. Monika Moskal, 2021, Remote. Sens..

CMS: Aboveground Biomass from Penobscot Experimental Forest, Maine, 2012

Andrew O. Finley, Aaron R. Weiskittel, Chad Babcock, 2016 .

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

Terje Gobakken, Erik Næsset, Andrew O. Finley, 2017, Remote Sensing of Environment.

Large-area hybrid estimation of aboveground biomass in interior Alaska using airborne laser scanning data

Liviu Theodor Ene, Terje Gobakken, Erik Næsset, 2018 .

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Andrew O. Finley, Chad Babcock, Christopher W. Woodall, 2016 .