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 Alberto Silva,Lee Alexandre Vierling,Luiz Carlos Estraviz Rodriguez,Adrián Cardil,Carlos Gonzalez-Benecke,Samuel De Padua Chaves Carvalho,Andrew Thomas Hudak,Carine Klauberg

doi:10.1186/s13021-017-0081-1

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.

Highlights

LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC)
LiDAR has been shown to be a powerful technology for AGC prediction in Eucalyptus plantations, and our results have demonstrated that highly accurate estimates of AGC can be achieved using LiDAR data
Our results show that a LiDAR pulse density of 5 pulses m−2 provides similar AGC prediction accuracy to that using a dataset with 10 pulses m−2 in fastgrowing eucalyptus plantations

Summary

Introduction

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 influ‐ ences AGC prediction using plot-based methods; 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. 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. The grid cell sizes for mapping ranging from 5 to 20 did not significantly affect the prediction accuracy of AGC at stand level in this system. Quantifying the substantial roles of fastgrowing Eucalyptus plantation on AGC stores, as sources of carbon emissions and as carbon sinks, has become key to understanding the global carbon cycle [6, 14]

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