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

Abstract

Background: LiDAR 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. Methods: LiDAR 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.

Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

“…Furthermore, intra-plot variance is captured at a pulse density of 0.5 pl m -2 , although small improvements are evident with increasing pulse density (Figure 23). These results are comparable to results found in similar studies (Goodwin et al, 2006;Treitz et al, 2012;Jakubowski et al, 2013;Watt et al, 2013;Hansen et al, 2015).…”

“…However, capture at multiple altitudes and aircraft speeds is limited by cost, particularly over large or discontinuous study areas such as in this study, and alternative modelling methods are required. A number of authors have decreased the number of points in a dataset to match a required point density (Maltamo et al, 2006;Tesfamichael et al, 2010;Watt et al, 2013); however this does not necessarily replicate the reduction in pulse density caused by change in altitude or aircraft speed if simulating anything other than a first-return dataset (Jakubowski et al, 2013). The majority of techniques that simulate a reduction in pulse density do so by superimposing a regular grid over the study area of a specified spatial resolution to attain required pulse densities, returns are then randomly selected from within each voxel (Gobakken and Naesset, 2008;Naesset, 2009;Korhonen et al, 2011;Jakubowski et al, 2013 (Baltsavias, 1999), particularly when simulating low pulse densities.…”

Estimación De Variables Forestales De Pinus Sylvestris L. En El Contexto De Un Inventario Forestal Aplicando Tecnología Lidar Aeroportada

“…Dado que el sistema cuenta con un GPS y un sistema inercial (IMU), se pueden calcular con una elevada precisión las coordenadas de todos los pulsos de energía reflejados por la superficie terrestre. De este modo es posible convertir los datos registrados en puntos con coordenadas x, y, z, (donde x,y proporcionan la ubicación espacial del punto, y z su altura) y analizar la distribución de las alturas para conseguir datos sobre la cubierta forestal o la superficie topográfica (Dubayah y Drake, 2000;Peterson et al, 2007;Maltamo et al, 2007;González-Ferreiro et al, 2012;Treitz et al, 2012;Watt et al, 2013).…”

EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

“…Different factors including the density and distribution of the source data, the interpolation algorithm and the grid resolution affect the accuracy of the DEMs (Watt et al 2013). Traditional surveying methods such as ground surveying and photogrammetric techniques can give high accuracy terrain data, however they are time consuming and labor intensive (Bilskie andHagen.…”

“…Light Detection and Ranging (LiDAR) technology has been increasingly used as an effective alternative to conventional optical remote sensing methods in accurately estimating above ground features where LiDAR measures the ranges to distant objects through measuring the time delay between the moment of transmission of the laser pulse and the moment of detection of the reflected signal (Wehr and Lohr, 1999). Compared to the DEM derived from photogrammetric techniques LiDAR DEM is more reliable and more accurate (Watt et al 2013, Guo et al, 2010, Liu and Zhang, 2008. The accuracy of LiDAR DEM is controlled by factors, such as topographic variability, sampling density, interpolation methods, spatial resolution, etc.…”