Comparison between an area-based and individual tree detection method for low-pulse density als-based forest inventory

Airborne laser scanning (ALS)-based forest inventories are being increasingly used. The two main approaches in deriving forest information from small-footprint laser scanner data are the area-based and individual tree detection (ITD) methods. In the present study we test the accuracies of an area-based k-nearest neighbour (k-NN)-method and ITD with a practical low-pulse density (1.8/m) ALS dataset at the plot level. The research material consists of 333 treewise measured circular plots from southern Finland. A test dataset of 97 plots was selected for stand characteristic accuracy observation. The root-mean-squared errors (RMSEs) for basal area, total volume, mean height and mean diameter were with ITD 26.7%, 26.9%, 8.2% and 12.2% and with kNN 23.1%, 23.7%, 12.4% and 14.8%, respectively. The results obtained in this study demonstrated that both ALS-based inventory methods are practical, even with low-pulse density data. A Combination method could be developed to utilize the strengths of both methods and should be further investigated. * Corresponding author.

[1]  HIGH-SCAN - Assessing forest stand attributes by integrated use of high-resolution satellite imagery and laserscanner.Contract No ENV4-CT98-0747 Data acquisition report.Report on WP 2, WP 4 and WP 5. , 2001 .

[2]  Sakari Tuominen,et al.  Performance of airborne laser scanning- and aerial photograph-based statistical and textural features in forest variable estimation , 2008 .

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

[4]  S. Popescu,et al.  Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass , 2003 .

[5]  M. Maltamo,et al.  Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data , 2006 .

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

[7]  Juha Hyyppä,et al.  Comparing thr accuracy of laser scanner with other optical remote sensing data sources for stand attributes retrieval , 2000 .

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

[9]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[10]  E. Tomppo,et al.  Satellite image-based national forest inventory of finland for publication in the igarss'91 digest , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[11]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[12]  J. Hyyppä,et al.  DETECTING AND ESTIMATING ATTRIBUTES FOR SINGLE TREES USING LASER SCANNER , 2006 .

[13]  Guangxing Wang,et al.  The calibration of digitized aerial photographs for forest stratification , 1998 .

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

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

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

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

[18]  J. Hyyppä,et al.  Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes , 2000 .

[19]  S. Magnussen,et al.  Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators , 1998 .

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

[21]  Jyrki Kangas,et al.  Kuviokohtaisten puustotunnusten ennustaminen laserkeilauksella , 1970 .

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

[23]  E. Næsset.  Determination of mean tree height of forest stands using airborne laser scanner data , 1997 .

[24]  J. Means.  Use of Large-Footprint Scanning Airborne Lidar To Estimate Forest Stand Characteristics in the Western Cascades of Oregon , 1999 .

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