The influence of LiDAR pulse density and plot size on the accuracy of New Zealand plantation stand volume equations

BackgroundLiDAR is an established technology that is increasingly being used to characterise spatial variation in important forest metrics such as total stem volume. The cost of forest inventory and LiDAR acquisition are strongly related to the inventory plot size and the LiDAR pulse density, respectively. It would therefore be beneficial to understand how reductions in these variables influence the strength of relationships between LiDAR and stand metrics. Although relatively high pulse densities are required for creating Digital Terrain Models (DTMs), once a DTM has been developed there is scope for reducing pulse density on subsequent flights to estimate stand metrics from LiDAR. This study used an extensive national dataset (for which the DTM had been characterised) obtained within New Zealand’s planted forests. Using this dataset, the objective of this research was to investigate how variation in both pulse density and plot size influence the precision of relationships between LiDAR metrics and total stem volume.MethodsLiDAR metrics were thinned to pulse densities ranging from 0.01 to 4 pulses m-2 across plot sizes ranging from 0.01 to 0.06 ha. For each pulse density/plot size combination regressions between LiDAR mean height and total stem volume were fitted using parameters fixed at values for the unthinned dataset or separately fitted for each pulse density/plot size combination.ResultsUsing the unthinned dataset (plot size = 0.06 ha; pulse density = 4 pulses m-2) the relationship between the mean LiDAR height and total stem volume had a coefficient of determination (R2) of 0.77. Thinning of the data had little effect on R2 above plot sizes of 0.03 ha and pulse densities of 0.1 pulses m-2. As pulse densities decreased below 0.1 pulses m-2 within plots of less than 0.025 ha, there was a sharp decline in R2 reaching values as low as 0.48 in plots of 0.01 ha with pulse densities of 0.01 pulses m-2. Simulations where parameters were fixed yielded almost identical R2 values to those where they were refitted for each plot size/pulse density combination. The number of pulses per plot integrates the effect of these two factors, with little change in the precision of the volume function until a threshold of 100 pulses per plot was reached.ConclusionsThis study showed that the precision of LiDAR-volume equations was relatively insensitive to reductions in pulse density and plot size when an accurate DTM was available. Acquisition of LiDAR information at lower pulse densities is likely to markedly improve the cost efficacy of this information for inventory purposes.

[1]  E. Næsset.  Estimating timber volume of forest stands using airborne laser scanner data , 1997 .

[2]  Nicholas C. Coops,et al.  Assessment of forest structure with airborne LiDAR and the effects of platform altitude , 2006 .

[3]  P. Beets,et al.  The inventory of carbon stock in New Zealand's post-1989 planted forest for reporting under the Kyoto protocol. , 2011 .

[4]  E. Næsset.  Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data , 2009 .

[5]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[6]  D. Donoghue,et al.  Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data , 2007 .

[7]  Mark O. Kimberley,et al.  Airborne scanning LiDAR in a double sampling forest carbon inventory , 2012 .

[8]  Terje Gobakken,et al.  Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data , 2008 .

[9]  Kevin Lim,et al.  LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada , 2012, Remote. Sens..

[10]  Erik Næsset,et al.  Effects of different flying altitudes on biophysical stand properties estimated from canopy height and density measured with a small-footprint airborne scanning laser , 2004 .

[11]  Projecting plots in LiDAR - Correcting for slope in remotely sensed data , 2012 .

[12]  Marek K. Jakubowski,et al.  Tradeoffs between lidar pulse density and forest measurement accuracy , 2013 .

[13]  E. Næsset.  Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[14]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[15]  M. Kimberley,et al.  ESTIMATION OF CARBON STOCKS IN NEW ZEALAND PLANTED FORESTS USING AIRBORNE SCANNING LIDAR , 2007 .

[16]  E. Tomppo.  National Forest Inventories : pathways for common reporting , 2010 .

[17]  P. Watt.  An evaluation of LiDAR and optical satellite data for the measurement of structural attributes in British upland conifer plantation forestry , 2005 .

[18]  J. Holmgrena,et al.  Large Scale Airborne Laser Scanning of Forest Resources in Sweden , 2004 .

[19]  Johan E. S. Fransson,et al.  Effects on estimation accuracy of forest variables using different pulse density of laser data , 2007 .

[20]  E. Næsset,et al.  Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve , 2002 .

[21]  R. Nelson,et al.  Determining forest canopy characteristics using airborne laser data , 1984 .

[22]  J. Means.  Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon , 1999 .