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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| Authors: | Carlos Alberto Silva Andrew Thomas Hudak Carine Klauberg Lee Alexandre Vierling Carlos Gonzalez-Benecke Samuel de Padua Chaves Carvalho Luiz Carlos Estraviz Rodriguez Adrián Cardil |
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| Institution: | 1.Department of Natural Resources and Society, College of Natural Resources,University of Idaho, (UI),Moscow,USA;2.US Forest Service (USDA), Rocky Mountain Research Station, RMRS,Moscow,USA;3.Department of Forest Engineering,Oregon State University,Corvallis,USA;4.College of Forestry,Federal University of Mato Grosso,Cuiabá,Brazil;5.Department of Forest Sciences, College of Agriculture Luiz de Queiroz (ESALQ),University of Sao Paulo (USP),Piracicaba,Brazil;6.Tecnosylva, Parque Tecnológico de León,León,Spain |
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| Abstract: | BackgroundLiDAR 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.ResultsThe 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.ConclusionLiDAR 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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