Accuracy of tree height estimation based on LIDAR data analysis

Some modern remote sensing technologies, including LIDAR (LIght Detection And Ranging), have significantly developed recently. Laser scanners mounted on the airborne platform make it possible to collect very precise information over large areas, including tree and stand heights. A literature review shows that the model-based method of tree height determination underestimates this parameter in comparison to field measurements. The objective of the study was to analyze accuracy of the automatic height estimation of Scots pine stands, based on the airborne laser scanning data and the example of the Milicz Forest District. Applied algorithm of the stand segmentation into individual trees gave systematic and significant underestimation of the number of trees. The minimum tree height was estimated with a large negative error reaching up to several meters. The maximum mean and top heights were determined more precisely, with a small negative error of a few percent. The sum of tree heights was determined with an error exceeding 40%, which is caused mostly by the error in estimation of the number of trees.

[1]  J. Hyyppä,et al.  Automatic detection of harvested trees and determination of forest growth using airborne laser scanning , 2004 .

[2]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[3]  Marco Heurich,et al.  Automated Tree Detection and Measurement in Temperate Forests of Central Europe Using Laserscanning Data , 2004 .

[4]  R. Wack,et al.  FOREST INVENTORY FOR EUCALYPTUS PLANTATIONS BASED ON AIRBORNE LASERSCANNER DATA , 2003 .

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

[6]  H. Weinacker,et al.  ACCURACY OF CROWN SEGMENTATION AND ESTIMATION OF SELECTED TREES AND FOREST STAND PARAMETERS IN ORDER TO RESOLUTION OF USED DSM AND NDSM MODELS GENERATED FROM DENSE SMALL FOOTPRINT LIDAR DATA , 2008 .

[7]  Alex C. Lee,et al.  Quantifying Australian forest floristics and structure using small footprint LiDAR and large scale aerial photography , 2006 .

[8]  Beat Koch,et al.  Development of Filtering , Segmentation and Modelling Modules for Lidar and Multispectral Data as a Fundament of an Automatic Forest Inventory System , 2004 .

[9]  Marco Heurich,et al.  FULL-WAVEFORM ANALYSIS OF SMALL FOOTPRINT AIRBORNE LASER SCANNING DATA IN THE BAVARIAN FOREST NATIONAL PARK FOR TREE SPECIES CLASSIFICATION , 2006 .

[10]  Nicholas C. Coops,et al.  Comparison of forest attributes extracted from fine spatial resolution multispectral and lidar data , 2004 .

[11]  B. Koch,et al.  TREESVIS-A SOFTWARE SYSTEM FOR SIMULTANEOUS 3 D-REAL-TIME VISUALISATION OF DTM , DSM , LASER RAW DATA , MULTISPECTRAL DATA , SIMPLE TREE AND BUILDING MODELS , 2004 .

[12]  Juha Hyyppä,et al.  Effects of flight altitude on tree height estimation using airborne laser scanning , 2004 .

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

[14]  R. Wack,et al.  Laser DTM generation for South-Tyrol and 3D-Visualization , 2005 .

[15]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[16]  E. Næsset,et al.  Estimating tree heights and number of stems in young forest stands using airborne laser scanner data , 2001 .