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Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar DataCombining 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.
Document ID
20170003294
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Babcock, Chad
(Washington Univ. Seattle, WA, United States)
Finley, Andrew O.
(Michigan State Univ. East Lansing, MI, United States)
Cook, Bruce D.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Weiskittel, Andrew
(Maine Univ. Orono, ME, United States)
Woodall, Christopher W.
(Forest Service Saint Paul, MN, United States)
Date Acquired
April 7, 2017
Publication Date
May 11, 2016
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 182
ISSN: 0034-4257
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN41134
Distribution Limits
Public
Copyright
Other

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