Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data

Chad Babcock, Andrew O. Finley, Bruce D. Cook, Aaron Weiskittel, Christopher W. Woodall

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalRemote Sensing of Environment
StatePublished - Sep 1 2016
Externally publishedYes

Bibliographical note

Funding Information:
Data for this study were provided by a unit of the Northern Research Station, U.S. Forest Service, located at the Penobscot Experimental Forest in Maine. Significant funding for collection of these data was provided by the U.S. Forest Service (USFS 15-JV-11242307-116 ). Andrew Finley was supported by National Science Foundation (NSF) DMS-1513481 , EF-1137309 , EF-1241874 , and EF-1253225 , as well as NASA Carbon Monitoring System grants.

Publisher Copyright:
© 2016 Elsevier Inc.


  • Bayesian hierarchical models
  • Biomass growth
  • Forest biomass
  • Gaussian process
  • Geospatial
  • LiDAR
  • Long-term forest inventory
  • Markov Chain Monte Carlo
  • Temporal misalignment


Dive into the research topics of 'Modeling forest biomass and growth: Coupling long-term inventory and LiDAR data'. Together they form a unique fingerprint.

Cite this