LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

Abstract

Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m −2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m −2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m −2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.

Estimación de variables de combustible de copa y de masa, caracterizando el efecto de las claras en su estructura usando LiDAR aerotransportado

“…The results of the stand height variables models H m and H 0 (R 2 adj = 0.95, RMSE = 0.385 m and R 2 adj = 0.91, RMSE = 0.506 m, respectively) are similar, in terms of R 2 of that achieved by Treitz et al (2010), and better than those provided by Gobakken and Naesset (2007) in Norway Spruce an Scots pine in Norway and González-Ferreiro et al (2012); although the goodness-of-fit statistics are lower than those achieved by Stephens et al (2008).…”

“…Something similar happened with the dg model (R 2 adj = 0.96, RMSE = 0.370 cm), which performed much better than those reported by Naesset (2002Naesset ( , 2004 for mean diameter and Gonçalves-Seco et al (2011) for mean and quadratic mean diameter, but in the order of the results obtained by Ruiz et al (2014b). Finally, stand basal area estimates derived from the linear model (R 2 adj = 0.97, RMSE = 1.33 m 2 ha -1 ) was similar to those achieved by Treitz et al (2010) in black spruce forests of Canada, but was even better than those reported by Naesset (2002), Lim et al (2003) for Canadian broadleaf forests, Stephens et al (2008) in radiata pine forests of New Zealand or Gonçalves-Seco et al (2011) and González-Ferreiro et al (2012) in Spain. The high crown diameter of adult P. pinaster trees and AB the regular and homogenous structure of these single-stratified stands could help to obtain good estimates of these parameters related with stand density.…”

“…The results of stand volume and aboveground biomass modelling (R and R 2 adj = 0.96, RMSE = 4.86 Mg ha -1 , respectively) are in the order of those achieved by Treitz et al (2010), but are better than those achieved by Hollaus et al (2007) in Austria alpine forests, Hall et al (2005) or González-Ferreiro et al (2012).…”

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

“…We showed in [34] that, for example, for a grid-cell size of 10 m × 10 m and a point density of 10 pts/m 2 , the derived RFDs match to ~95% with RFDs based on much higher point densities Whether this loss of information is significant or important still depends on the research question and/or the planned application. For example, Wilkes et al [70], Hawbaker et al [71], and Treitz et al [42] pointed out that lower point densities (<5 pts/m 2 ) are sufficient for canopy-structure analysis regarding tactical forest management, but these findings are only valid for canopy-structure descriptors derived on larger scales than the one we used in this study. Hayashi et al [72], in contrast, concluded that in complex forests, low-density ALS data was ineffective in estimating the defined canopy-structure descriptors with sufficient accuracy.…”

“…Most canopy-structure components, however, have inherent spatial scales and the choice of the spatial scale should be made considering the investigated structural component [40,41]. Nevertheless, an acceptable compromise often needs to be found between the technical capabilities of ALS and user needs, such as the application domain or cost-benefit considerations [42,43]. In this study, ABAs represented in regularly spaced grids were used, allowing flexible analysis in terms of spatial scales and cross-scale comparisons (e.g., testing reproducible up-and down-scaling methods).…”