Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions

By Matti Maltamo, Kalle Eerikäinen, Juho Pitkänen, Juha Hyyppä, M. Vehmas. 2004

362 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3fee6be3958b8795528d81175701f6a2da7d8a9b

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data (2011, background): “Airborne laser scanning (ALS) has recently led to a revolution in remote sensing technology for characterizing the canopy structure, due to its ability to measure three-dimensional information (Koukoulas & Blackburn, 2005; Maltamo et al., 2004).”
Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables (2016, methodology): “…other studies have reported the benefits of 556 using ITC estimates of crown area in addition to variables related to the distribution of height 557 values in the prediction of forest structural characteristics, such as mean DBH and basal area 558 (e.g. Hyyppä et al., 2001; Maltamo et al., 2004).”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, background): “Maltamo et al. (2004), Lindberg et al. (2010), Vauhkonen et al. (2010) and Vastaranta et al. (2012) combined ABA and ITD with the purpose of overcoming this difficulty and improving estimation accuracy.”
A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments (2007, background): “An automated and objective LiDAR classification algorithm that appropriately balances commission omission errors will facilitate applications that exploit the 3-”
Weibull and percentile models for lidar-based estimation of basal area distribution (2005, result): “…of the largest of the individual trees of a stand from laser data with estimation of tree numbers in diameter classes of the smaller trees using theoretical diameter distribution model, Maltamo et al. (2004) reported a RMSE for plot volume of 16%, which is in accordance with the present results.”
Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using High-Resolution Portable Scanning Lidar (2006, background): “In contrast, commercially available aircraft-mounted lidar instruments with fine spatial resolution can provide precise profiles of canopy height [12]–[15].”
Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data (2010, background): “The tree detection rate may vary in mature forests (Persson et al. 2002; Maltamo et al. 2004), and where the canopy is closed, better results may in general be gained using area-based approaches (e.”
Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR (2005, methodology): “…airborne LiDAR data offered the possibility to detect most canopy trees and hardly any subcanopy trees of smaller height, such as suppressed or intermediate trees (Holmgren et al. 2003a; Maltamo et al. 2004; McCombs et al. 2003; Persson et al. 2002; Takahashi et al. 2005; Yone et al. 2002).”
Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park (2008, background): “One example would be to introduce a parametric-Weibull function (Maltamo et al., 2004).”
Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data (2009, background): “…can be obtained when ALS data are used at the tree level, although only a proportion of the individual trees in the standing stock can be detected in this way and a model chain is needed to derive forest inventory end products (Persson et al. 2002, Maltamo et al. 2004a, 2007, Solberg et al. 2006).”
Capturing tree crown formation through implicit surface reconstruction using airborne lidar data (2009, neutral): “…et al., 2004), detection of single or multi-story stand condition (Zimble et al., 2003), estimation of carbon density (Patenaude et al., 2004), biomass estimation (Bortolot & Wynne, 2005), and modeling the distribution of understory vegetation (Gobakken & Næsset, 2004, 2005; Maltamo et al., 2004).”
Estimating stand volume in broad-leaved forest using discrete-return LiDAR: plot-based approach (2009, methodology): “Two approaches can be adopted with respect to the estimation using discrete-return lidar: (1) individual treebased (e.g., Persson et al. 2002; Maltamo et al. 2004; Popescu et al. 2003, 2004) and (2) stand/plot-based (e.g., Nelson et al. 1988; Næsset 1997, 2002; Means et al. 2000; Lim et al. 2003a;…”
The role of LiDAR in sustainable forest management (2008, background): “…2002 6 Individual tree Douglas-fir, ponderosa pine Andersen et al. 2006 Volume 4-5 Individual tree Norway spruce, Scots pine, silver birch, downy birch Maltamo et al. 2004 Pathological indicators Not recommended using LiDAR(e.g. scars, broken tops) Growth 10 Individual tree Scots pine Yu et al.…”
Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas (2008, background): “LIDAR is an effective information source for studies related to tree height, forest structure, biomass, and all the parameters that are mainly related to the vertical dimension of the scene under analysis (e.g., [ 22 ]‐[25]).”