Erratum to: Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data

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Abstract

Erratum to: Carbon Balance Manage (2017) 12:13 DOI 10.1186/s13021-017-0081-1 Upon publication of the original article [1], the authors noticed that Figure 1 had accidentally been changed to Figure 5. Please see the correct Fig. 1 in this erratum. Fig. 1 Location of the study area in the State of São Paulo, Brazil. The stars indicate the location of the Eucalyptus plantations This has also been updated in the original article. We apologise for the error made.

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Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data

Carlos Silva, Andrew Hudak, Carine Klauberg … (2017)

Background 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. Results 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. Conclusion 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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Author and article information

Contributors

Carlos Alberto Silva: +1(626) 3724580 , carlos_engflorestal@outlook.com

Andrew Thomas Hudak: ahudak@fs.fed.us

Carine Klauberg: carine_klauberg@hotmail.com

Lee Alexandre Vierling: leev@uidaho.edu

Carlos Gonzalez-Benecke: Carlos.Gonzalez@oregonstate.edu

Samuel de Padua Chaves Carvalho: sam.padua@gmail.com

Luiz Carlos Estraviz Rodriguez: lcer@usp.br

Adrián Cardil: adriancardil@gmail.com

Journal

Journal ID (nlm-ta): Carbon Balance Manag

Journal ID (iso-abbrev): Carbon Balance Manag

Title: Carbon Balance and Management

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 1750-0680

Publication date (Electronic): 30 June 2017

Publication date PMC-release: 30 June 2017

Publication date Collection: December 2017

Volume: 12

Electronic Location Identifier: 14

Affiliations

[1 ]ISNI 0000 0001 2284 9900, GRID grid.266456.5, Department of Natural Resources and Society, College of Natural Resources, , University of Idaho, (UI), ; 875 Perimeter Drive, Moscow, ID 83843 USA

[2 ]US Forest Service (USDA), Rocky Mountain Research Station, RMRS, 1221 South Main Street, Moscow, ID 83843 USA

[3 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Forest Engineering, , Oregon State University, ; 269 Peavy Hall, Corvallis, OR 97331 USA

[4 ]ISNI 0000 0001 2322 4953, GRID grid.411206.0, College of Forestry, , Federal University of Mato Grosso, ; Av. Fernando Correa da Costa, 2367, Boa Esperança, Cuiabá, MT 78060-900 Brazil

[5 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Department of Forest Sciences, College of Agriculture Luiz de Queiroz (ESALQ), , University of Sao Paulo (USP), ; Av. Pádua Dias, 11, Piracicaba, SP 13418-900 Brazil

[6 ]Tecnosylva, Parque Tecnológico de León, 24009 León, Spain

Article

Publisher ID: 82

DOI: 10.1186/s13021-017-0082-0

PMC ID: 5493598

PubMed ID: 28667472

SO-VID: c8fd6bc4-8089-4995-96fb-46d0be89a30e

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 6 June 2017

Date accepted : 13 June 2017

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ScienceOpen disciplines: Environmental change

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ScienceOpen disciplines: Environmental change

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