Estimation of Tree Height and Stem Volume on Plots Using Airborne Laser Scanning

By Johan Holmgren, Mats B. Nilsson, Håkan Olsson. Forestry sciences, 2003

229 citations recorded by the source corpus.

Airborne laser scanning has the ability to measure the vertical and horizontal structure of forest vegetation. The aim of this study is to investigate how measures derived from laser scanning data can be used in regression models for estimation of basal-area-weighted mean tree height and stem volume on 10 m radius field plots. Influence of laser scan angle on tree height estimation and on crown coverage area estimation is also investigated. The study area was located in southern Sweden (lat. 58°30′N, long. 13°40′E). The dominating tree species were Norway spruce (Picea abies L. Karst .), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). Linear regression functions (R2=0.89–0.91) used to predict basal-area-weighted mean tree height had a Root Mean Square Error (RMSE) of 1.45–1.56 m, corresponding to 10–11% of average height. Scanning angle was not significant for estimation of basal-area-weighted mean tree height. Two regression models were used for prediction of stem volume. The first model (R2 = 0.90), with laser derived mean height together with laser derived crown coverage area as predicting variables, gave RMSE of 37 m3 ha-1, corresponding to 22% of average stem volume. The second model (R2=0.82), with laser derived tree height together with laser derived stem number as predicting variables, gave RMSE of 43 m3 ha-1, corresponding to 26% of average stem volume. The results implies that airborne laser scanning, if combined with a field sample, has potential of retrieving information with high spatial resolution (10 m radius plot) about tree height and stem volume for a forest area. FOR. SCI. 49(3):419–428.

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

View Paper: https://www.semanticscholar.org/paper/5b7d660afd8c751ce38013a9742a4299410b99b0

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Citations in the corpus

Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates (2014, methodology): “As Figure 1 shows, there is some discrepancy when defining field sampling plot size when working with LiDAR data [5,9–31].”
Practical large-scale forest stand inventory using a small-footprint airborne scanning laser (2004, methodology): “…tree height (Næsset 1997a , 2002, Magnussen & Boudewyn 1998, Magnussen et al. 1999, Means et al. 2000, Næsset & Bjerknes 2001, Næsset & Økland 2002, Holmgren et al. 2003), stand basal area (Means et al. 2000, Næsset 2002) and stand volume (Næsset 1997b, 2002, Means et al. 2000, Holmgren et al.…”
Review of optical-based remote sensing for plant trait mapping (2013, background): “…/ canopy height [m] Distance between ground and the upper boundary of the main photosynthetic tissue Directly derived from laser scanning data (Holmgren et al., 2003; Næsset et al., 2004; Kaartinen et al., 2012; Straatsma and Middelkoop, 2007) * * * 4-5 Indirectly related to leaf area index…”
Detection of individual trees and estimation of tree height using LiDAR data (2007, background): “…South Korea e-mail: leewk@korea.ac.kr J.-H. Lee G. S. Biging P. Gong Division of Environmental Science, Policy and Management, University of California at Berkeley, Berkeley, CA 94720, USA such as tree heights, sub-canopy topographies, and distributions, with high precision (Holmgren et al. 2003).”
Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR (2005, methodology): “In order to ensure that the regression models were not overfitted, a ratio (Q value) used by Holmgren et al. (2003a) was calculated as follows: the standard deviation of the error with logarithmic scale obtained from cross validation was divided by the standard deviation of the error with…”
Weibull and percentile models for lidar-based estimation of basal area distribution (2005, methodology): “…timber volume by different airborne laser profiling and scanning systems (e.g. Nelson et al., 1988; Næsset, 1997b, 2002b, 2004b; Magnussen & Boudewyn, 1998; Lefsky et al., 1999; Magnussen et al., 1999; Means et al., 1999, 2000; Næsset & Bjerknes, 2001; Næsset & Økland, 2002; Holmgren et al., 2003).”
A voxel-based lidar method for estimating crown base height for deciduous and pine trees (2008, background): “…to derive measurements, such as tree height and crown dimensions, at stand level (Hall et al., 2005; Naesset & Bjerknes, 2001), plot level (Holmgren et al., 2003; Hyyppä et al., 2001; Lim & Treitz, 2004; Popescu et al., 2004), or individual tree level (Chen et al., 2006; Coops et al.,…”
Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities (2012, background): “…between variables commonly used in forest planning such as mean height (Næsset, 1997a, 2002, 2004; Magnussen et al., 1999; Næsset and Bjerknes, 2001; Holmgren et al., 2003; Hall et al., 2005; Stephens et al., 2007; Treitz et al., 2010); mean diameter (Næsset, 2002, 2004); quadratic mean diameter…”
Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA (2004, background): “…very intense and resulted in a series of studies that demonstrated that lidar technology is well suited for providing estimates of forest biophysical parameters (Næsset and Bjerknes 2001, Næsset and Økland 2002, Persson et al. 2002, Popescu et al. 2002, Holmgren et al. 2003, McCombs et al. 2003).”
Three-dimensional surface reconstruction of tree canopy from lidar point clouds using a region-based level set method (2013, background): “Additional problems include surface irregularities on the CHM (Ben-Arie et al. 2009), tree top dislocation (Pitkanen et al. 2004), and underestimation of tree height (Næsset1997; Holmgren, Nilsson, and Olsson 2003).”
Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning (2004, methodology): “Forest variables have been predicted in Sweden using airborne laser scanner data, with methods based on both detection of individual trees and laser variables derived from a canopy model (Holmgren et al. 2003).”