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Carlos Gonzalez-Benecke   Dr.  University Educator/Researcher 
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Carlos Gonzalez-Benecke published an article in June 2017.
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Andrew T. Hudak

28 shared publications

Forestry Sciences Laboratory, Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture, 1221 South Main St., Moscow, ID 83843, USA

Carlos Alberto Silva

22 shared publications

Biosciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20707, USA

Lee A. Vierling

15 shared publications

Department of Forest, Rangeland, and Fire Sciences, McCall Outdoor Science School, University of Idaho, Moscow, Idaho, United States of America

Adrián Cardil

7 shared publications

University of Lleida, Spain

Carine Klauberg

5 shared publications

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Publication Record
Distribution of Articles published per year 
(2017)
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Publications
Article 0 Reads 0 Citations Erratum to: Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-gro... Carlos Alberto Silva, Andrew Thomas Hudak, Carine Klauberg, ... Published: 30 June 2017
Carbon Balance and Management, doi: 10.1186/s13021-017-0082-0
DOI See at publisher website PubMed View at PubMed
Article 0 Reads 7 Citations Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyp... Carlos Alberto Silva, Andrew Thomas Hudak, Carine Klauberg, ... Published: 07 June 2017
Carbon Balance and Management, doi: 10.1186/s13021-017-0081-1
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m−2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. The results show that LiDAR pulse density of 5 pulses m−2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m−2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m−2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.
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