Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

By Erik Næsset. 2004

583 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/49ff9d98f34a220ff4f0364fed1ea293db31e46d

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In Environmental Engineering

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Citations in the corpus

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations (2017, background): “A plethora of studies show strong relationships between lidar metrics and forest variables (Asner et al., 2009; Babcock et al., 2013; Finley et al., 2014b, 2017; Lim et al., 2003; Næsset, 2004, 2011).”
Inventory of Small Forest Areas Using an Unmanned Aerial System (2015, background): “, p100), height standard deviation (hsd), and density variables [52].”
Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities (2012, background): “…et al., 2010); dominant height (Næsset, 2002, 2004; Lovell et al., 2005; Stephens et al., 2007; Rombouts et al., 2008; Treitz et al., 2010); stem number (Næsset, 2002, 2004; Hall et al., 2005; Treitz et al., 2010); total aboveground biomass (Hall et al., 2005; Treitz et al., 2010); foliage…”
Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia (2007, methodology): “2 m3 ha 1 2.6 11.5 Næsset (2004a)a 65 Stand volume Mature pine-dominated 1.0 37 19 162.”
Analysis of full-waveform LiDAR data for forestry applications: a review of investigations and methods (2011, methodology): “The Si metrics are divided into two sub-groups, Sih and Sid, which are both inspired by the tree characterization metrics developed by Naesset (2004).”
Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data (2011, background): “Stepwise multiple linear regressions between two LiDAR parameters, notably the height quantiles (Lim & Treitz, 2004; Magnussen & Boudewyn, 1998; Næsset, 2004) and fc, and forest measurements of Ht, CW, FBH, and SD were carried out.”
Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest (2013, methodology): “The major independent variables for stepwise multiple regression in this study were the mean height and height quantiles from LiDAR points [31,32].”
Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway (2011, methodology): “The canopy densities were then computed as the proportion of number of returns above a given layer to total number of returns including those below 1.3 m, see Næsset (2004a) for further details.”