Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands

Airborne laser scanning based forest inventories employ two major methods: individual tree detection (ITD) and the area-based statistical approach (ABSA). ITD is based on the assumption that trees are of a certain form and can be delineated using airborne laser scanning techniques, whereas ABSA is an empirical method based on the relations between area-level forest attributes and laser echo height distributions. These two methods are compared here within the same test area in terms of their usefulness for estimating mean forest stand characteristics and tree size distributions. All evaluations were performed using leave-one-out cross validation. The average errors in volume and basal area did not differ significantly between the methods. ABSA resulted in overall better accuracies when estimating the diameter and height of the basal area median tree and the number of stems, whereas ITD produced significantly biased estimates for the number of stems and the mean tree size. Tree size distributions were estim...

[1]  J. Means,et al.  Predicting forest stand characteristics with airborne scanning lidar , 2000 .

[2]  Thomas E. Burk,et al.  Goodness-of-Fit Tests and Model Selection Procedures for Diameter Distribution Models , 1988, Forest Science.

[3]  Timo Pukkala,et al.  Using Numerical Optimization for Specifying Individual-Tree Competition Models , 2000, Forest Science.

[4]  R. Hill,et al.  Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data , 2003 .

[5]  E. Næsset,et al.  Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data , 2010 .

[6]  Lauri Mehtätalo,et al.  Eliminating the effect of overlapping crowns from aerial inventory estimates , 2006 .

[7]  Jari Vauhkonen,et al.  Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data , 2010 .

[8]  S. R. Searle,et al.  Generalized, Linear, and Mixed Models , 2005 .

[9]  M. Maltamo,et al.  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 .

[10]  K. Lim,et al.  Lidar remote sensing of biophysical properties of tolerant northern hardwood forests , 2003 .

[11]  S. R. Searle,et al.  Generalized, Linear, and Mixed Models: McCulloch/Generalized, Linear, and Mixed Models , 2005 .

[12]  M. Maltamo,et al.  The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs , 2007 .

[13]  Lauri Mehtätalo,et al.  RECOVERING PLOT-SPECIFIC DIAMETER DISTRIBUTION AND HEIGHT- DIAMETER CURVE USING ALS BASED STAND CHARACTERISTICS , 2007 .

[14]  P. Axelsson DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[15]  J. W. Flewelling,et al.  Probability models for individually segmented tree crown images in a sampling context. , 2008 .

[16]  E. Næsset Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data , 2002 .

[17]  Jouko Laasasenaho Taper curve and volume functions for pine, spruce and birch [Pinus sylvestris, Picea abies, Betula pendula, Betula pubescens] , 1982 .

[18]  J. Holmgren,et al.  Estimation of tree lists from airborne laser scanning data using a combination of analysis on single tree and raster cell level. , 2008 .

[19]  J. Hyyppä,et al.  Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions , 2004 .

[20]  Jussi Peuhkurinen,et al.  Predicting Tree Attributes and Quality Characteristics of Scots Pine Using Airborne Laser Scanning Data , 2009 .

[21]  J. Hyyppä,et al.  Estimation of stem volume using laser scanning-based canopy height metrics , 2006 .

[22]  E. Næsset,et al.  Non-parametric prediction of diameter distributions using airborne laser scanner data , 2009 .

[23]  K. Eerikäinen,et al.  A calibrateable site index model for Pinus kesiya plantations in southeastern Africa , 2002 .

[24]  M. Maltamo,et al.  Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs , 2008 .

[25]  E. Næsset Estimating timber volume of forest stands using airborne laser scanner data , 1997 .

[26]  J. Holmgren Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning , 2004 .

[27]  Juha Hyyppä,et al.  Comparison between an area-based and individual tree detection method for low-pulse density als-based forest inventory , 2009 .

[28]  L. Mehtätalo Height-diameter models for Scots pine and birch in Finland , 2005 .

[29]  A. Hudak,et al.  Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data , 2008 .

[30]  Jussi Peuhkurinen,et al.  Preharvest measurement of marked stands using airborne laser scanning , 2007 .

[31]  Juha Hyyppä,et al.  The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve , 2004 .

[32]  Paul E. Gessler,et al.  The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data , 2008 .

[33]  Åsa Persson,et al.  Identifying species of individual trees using airborne laser scanner , 2004 .

[34]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[35]  J. Lappi Calibration of Height and Volume Equations with Random Parameters , 1991, Forest Science.

[36]  J. Nyblom,et al.  Estimating forest attributes using observations of canopy height: a model-based approach. , 2009 .

[37]  Steen Magnussen,et al.  Recovering Tree Heights from Airborne Laser Scanner Data , 1999, Forest Science.

[38]  Petteri Packalen,et al.  Comparison of individual tree detection and canopy height distribution approaches: a case study in Finland. , 2008 .

[39]  D. A. Hill,et al.  Combined high-density lidar and multispectral imagery for individual tree crown analysis , 2003 .

[40]  Arnon Karnieli,et al.  redicting forest structural parameters using the image texture derived from orldView-2 multispectral imagery in a dryland forest , Israel , 2011 .

[41]  B. Koch,et al.  Detection of individual tree crowns in airborne lidar data , 2006 .

[42]  M. Rautiainen,et al.  Estimation of forest canopy cover: A comparison of field measurement techniques , 2006 .

[43]  D. Cox,et al.  The statistical analysis of series of events , 1966 .

[44]  M. Maltamo,et al.  Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics , 2010 .

[45]  T. Tokola,et al.  Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information , 2005 .

[46]  John M. Gauch,et al.  Image segmentation and analysis via multiscale gradient watershed hierarchies , 1999, IEEE Trans. Image Process..