Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning

By Johan Holmgren. 2004

280 citations recorded by the source corpus.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/b4760d68d26b60c6ecce94588e351523da578089

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Estimation of stem volume using laser scanning-based canopy height metrics (2006, methodology): “Furthermore, volume estimates derived from fi eld assessments were considerably more inaccurate than laser-based estimates, which correspond to the results of earlier studies implemented in other Nordic countries (e.g. Næsset, 2002 , 2004 ; Holmgren, 2004 ).”
Obtaining plotwise mean height and volume growth in boreal forests using multi‐temporal laser surveys and various change detection techniques (2008, background): “( 2003 ) and Holmgren ( 2004 ) have shown that this approach produces highly reliable estimates of stand variables.”
Weibull and percentile models for lidar-based estimation of basal area distribution (2005, methodology): “…(Næsset & Bjerknes, 2001; Næsset, 2002b), and empirical studies have indicated that characteristics such as mean tree height, mean stem diameter and stand volume may be derived from laser data with similar or even better accuracy than conventional methods (Næsset, 2002b , 2004b ; Holmgren, 2004).”
Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data (2008, methodology): “There are also a few other studies considering the effects of pulse density on plot-level assessments of forest structure (Holmgren, 2004; Lovell et al., 2005; Maltamo et al., 2006; Magnusson et al., 2007; Gobakken and Næsset, 2007).”
Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data (2009, methodology): “, 2003), basal area (Lefsky et al., 1999; Næsset, 2002; Lim et al., 2003a; Holmgren, 2004; Jensen et al., 2006), bole diameter at breast height (Lefsky et al.”
A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (2013, unknown): “…approaches may be used to build predictive models, but the most common methods used are either parametric (e.g., Means et al. 2000; Næsset 2002; Holmgren 2004; Næsset et al. 2011; Woods et al., 2011) or non-parametric (e.g., Packalén and Maltamo 2007; Hudak et al. 2008; Frazer et al. 2011a;…”
Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates (2014, background): “A number of studies have been published, analyzing the effect of flight parameters [6,28,30] or LiDAR data density [5,6,15,33–35] on the forest stand attribute estimation accuracy.”
Estimating stand volume in broad-leaved forest using discrete-return LiDAR: plot-based approach (2009, methodology): “Height and density variables derived from lidar data similar to in previous studies (e.g., Næsset 1997, 2002; Means et al. 2000; Holmgren 2004) were examined, except for dOVF, which was newly proposed in the present study.”
Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data (2015, background): “Holmgren (2004), Maltamo, Eerikainen, Packalén, and Hyyppä (2006), and Gobakken and Næsset (2008) all demonstrated that pulse densities of 0.1 pulse per m2 and greater are adequate for estimating forest attributes such as basal area and growing stock volume, both of which are closely related to AGB.”