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iForest - Biogeosciences and Forestry

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Three-dimensional forest stand height map production utilizing airborne laser scanning dense point clouds and precise quality evaluation

Umut G Sefercik (1)   , Ayhan Atesoglu (2)

iForest - Biogeosciences and Forestry, Volume 10, Issue 2, Pages 491-497 (2017)
doi: https://doi.org/10.3832/ifor2039-010
Published: Apr 12, 2017 - Copyright © 2017 SISEF

Research Articles


In remote sensing, estimation of the forest stand height is an ever-challenging issue due to the difficulties encountered during the acquisition of data under forest canopies. Stereo optical imaging offers high spatial and spectral resolution; however, the optical correlation is lower in dense forests than in open areas due to an insufficient number of matching points. Therefore, in most cases height information may be missing or faulty. With their long wavelengths of 0.2 to 1.3 m, P-band and L-band synthetic aperture radars are capable of penetrating forest canopies, but their low spatial resolutions restrict the use of single-tree based forest applications. In this study, airborne laser scanning was used as an effective remote sensing technique to produce large-scale maps of forest stand height. This technique produces very high-resolution point clouds and has a high penetration capability that allows for the detection of multiple echoes per laser pulse. A study area with a forest coverage of approximately 60% was selected in Houston, USA, and a three-dimensional color-coded map of forest stands was produced using a normalized digital surface model technique. Rather than being limited to the number of ground control points, the accuracy of the produced map was assessed with a model-to-model approach using terrestrial laser scanning. In the accuracy assessment, the standard deviation was used as the main accuracy indicator in addition to the root mean square error and normalized median absolute deviation. The absolute geo-location accuracy of the generated map was found to be better than 1 cm horizontally and approximately 40 cm in height. Furthermore, the effects of bias and relative standard deviations were determined. The problems encountered during the production of the map, as well as recommended solutions, are also discussed in this paper.

  Keywords


Airborne Laser Scanning, Forest Stand Height Map, First Echo, Last Echo, NDSM

Authors’ address

(1)
Umut G Sefercik
Department of Geomatics Engineering, Bulent Ecevit University, 67100 Zonguldak (Turkey)
(2)
Ayhan Atesoglu
Department of Forest Engineering, Faculty of Forestry, Bartin University, 74100 Bartin (Turkey)

Corresponding author

 
Umut G Sefercik
sefercik@beun.edu.tr

Citation

Sefercik UG, Atesoglu A (2017). Three-dimensional forest stand height map production utilizing airborne laser scanning dense point clouds and precise quality evaluation. iForest 10: 491-497. - doi: 10.3832/ifor2039-010

Academic Editor

Piermaria Corona

Paper history

Received: Mar 03, 2016
Accepted: Jan 27, 2017

First online: Apr 12, 2017
Publication Date: Apr 30, 2017
Publication Time: 2.50 months

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(1)
Akay AE, Wing M, Sessions J (2012)
Estimating structural properties of riparian forests with airborne LiDAR data. International Journal of Remote Sensing 33 (22): 7010-7023.
CrossRef | Gscholar
(2)
ASPRS (2014)
ASPRS positional accuracy standards for digital geospatial data. Photogrammetric Engineering and Remote Sensing 81 (3): A1-A26.
Gscholar
(3)
Baligh A, Valadan Zoej MJ, Mohammadzadeh A (2008)
Bare earth extraction from airborne lidar data using different filtering methods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37 (B3b): 237-240.
Online | Gscholar
(4)
Baltsavias E (1999)
A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 54 (2): 83-94.
CrossRef | Gscholar
(5)
Glennie C, Carter WE, Shrestha RL, Dietrich WE (2013)
Geodetic imaging with airborne LiDAR: the Earth’s surface revealed. Reports on Progress in Physics 76 (8): 086801.
CrossRef | Gscholar
(6)
Hill JM, Graham LA, Henry RJ, Cotter DM, Ping A, Young P (2000)
Wide-area topographic mapping and applications using airborne light detection and ranging (Lidar) technology. Photogrammetric Engineering and Remote Sensing 66 (8): 908-914.
Online | Gscholar
(7)
Höhle J, Höhle M (2009)
Accuracy assessment of digital elevation models by means of robust statistical methods. ISPRS Journal of Photogrammetry and Remote Sensing 64: 398-406.
CrossRef | Gscholar
(8)
Jacobsen K (2003)
DEM generation from satellite data. In: Proceedings of the “23rd EARSeL Symposium 2003: Remote Sensing in Transition” (Goossens R eds). Ghent (Belgium) 2-5 June 2003. Millpress, Rotterdam, Netherlands, pp. 513-525. [ISBN 90-5966-007-2]
Online | Gscholar
(9)
Jacobsen K (2012)
Characteristics of nearly world wide available digital height models. In: Proceedings of the “10th Seminar on Remote Sensing and GIS Applications in Forest Engineering”. Curitiba (Brazil) 15-18 Oct 2012, pp. 8.
Online | Gscholar
(10)
Koch B, Heyder U, Welnacker H (2006)
Detection of individual tree crowns in airborne LiDAR data. Photogrammetric Engineering and Remote Sensing 72: 357-363.
CrossRef | Gscholar
(11)
Lin Q, Vesecky JF, Zebker HA (1994)
Comparison of elevation derived from insar data with DEM over large relief terrain. International Journal of Remote Sensing 15 (9): 1775-1790.
CrossRef | Gscholar
(12)
Liu X (2008)
Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography 32: 31-49.
CrossRef | Gscholar
(13)
Lohr U (1998)
Digital elevation models by laser scanning. The Photogrammetric Record 16 (91): 105-109.
CrossRef | Gscholar
(14)
Mandlburger G, Briese C, Pfeifer N (2007)
Progress in LiDAR sensor technology - chance and challenge for DTM generation and data administration. In: Proceedings of the “51st Photogrammetric Week ’07” (Fritz D ed). Wichmann Verlag, Heidelberg, Germany, pp. 159-169.
Gscholar
(15)
McIntosh K, Krupnik A, Schenk A (2000)
Improvement of automatic DSM generation over urban areas using airborne laser scanner data. International Archives of Photogrammetry and Remote Sensing 33 (B3): 563-570.
Online | Gscholar
(16)
Shan J, Sampath A (2005)
Urban DEM generation from raw lidar data: a labeling algorithm and its performance. Photogrammetric Engineering and Remote Sensing 71 (2): 217-226.
CrossRef | Gscholar
(17)
Smreček R (2012)
Utilization of ALS data for forestry purposes. In: Proceedings of the “6th GI Forum 2012: Geovizualisation, Society and Learning” (Jekel T, Car A, Strobl J, Griesebner G eds). Salzburg (Austria) 2-6 July 2012. H. Wichmann Verlag, VDE Verlag GMBH, Berlin, Offenbach, Germany, pp. 365-375.
Online | Gscholar
(18)
Sterenczak K, Bedkowski K, Weinacker H (2008)
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. International Archives of Photogrammetry and Remote Sensing 37 (B6b): 27-32.
Online | Gscholar
 

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