Abstract
During the past decade, airborne laser scanning (ALS) has established its status as the state-of-the-art method for detailed forest mapping and monitoring. Current operational forest inventory widely utilizes ALS-based methods. Recent advances in sensor technology and image processing have enabled the extraction of dense point clouds from digital stereo imagery (DSI). Compared with ALS data, the DSI-based data are cheap and the point cloud densities can easily reach that of ALS. In terms of point density, even the high-altitude DSI-based point clouds can be sufficient for detecting individual tree crowns. However, there are significant differences in the characteristics of ALS and DSI point clouds that likely affect the accuracy of tree detection. In this study, the performance of high-altitude DSI point clouds was compared with low-density ALS in detecting individual trees. The trees were extracted from DSI- and ALS-based canopy height models (CHM) using watershed segmentation. The use of both smoothed and unsmoothed CHMs was tested. The results show that, even though the spatial resolution of the DSI-based CHM was better, in terms of detecting the trees and the accuracy of height estimates, the low-density ALS performed better. However, utilizing DSI with shorter ground sample distance (GSD) and more suitable image matching algorithms would likely enhance the accuracy of DSI-based approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ackermann F (1999) Airborne laser scanning—present status and future expectations. ISPRS J Photogramm Remote Sens 54(2):64–67
Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. Int Arch Photogramm Remote Sens 33(B4/1; PART 4):111–118
Gaveau DL, Hill RA (2003) Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Can J Remote Sens 29(5):650–657
Held A, Ticehurst C, Lymburner L, Williams N (2003) High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing. Int J Remote Sens 24(13):2739–2759
Hirschmüller H (2008) Stereo processing by semiglobal matching and mutual information. IEEE Trans Pattern Anal Mach Intell 30(2):328–341
Holopainen M, Vastaranta M, Hyyppä J (2014) Outlook for the next generation’s precision forestry in Finland. Forests 5(7):1682–1694
Hyyppä J, Inkinen M (1999) Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finl 16(2):27–42
Hyyppä J, Kelle O, Lehikoinen M, Inkinen M (2001) A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans Geosci Remote Sens 39(5):969–975
Järnstedt J, Pekkarinen A, Tuominen S, Ginzler C, Holopainen M, Viitala R (2012) Forest variable estimation using a high-resolution digital surface model. ISPRS J Photogramm Remote Sens 74:78–84
Kaartinen H, Hyyppä J, Yu X, Vastaranta M, Hyyppä H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Næsset E (2012) An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens 4(4):950–974
Koch B, Heyder U, Weinacker H (2006) Detection of individual tree crowns in airborne lidar data. Photogramm Eng Remote Sens 72(4):357–363
Korpela I (2004) Individual tree measurements by means of digital aerial photogrammetry, vol 3. Finnish Society of Forest Science Helsinki, Finland
Lim K, Treitz P, Wulder M, St-Onge B, Flood M (2003) LiDAR remote sensing of forest structure. Prog Phys Geogr 27(1):88–106
Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80(1):88–99
Nurminen K, Karjalainen M, Yu X, Hyyppä J, Honkavaara E (2013) Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables. ISPRS J Photogramm Remote Sens 83:104–115
Olofsson K, Lindberg E, Holmgren J (2008) A method for linking field-surveyed and aerial-detected single trees using cross correlation of position images and the optimization of weighted tree list graphs. In: Proceedings of SilviLaser 2008: 8th international conference on LiDAR applications in forest assessment and inventory, Edinburgh, UK, 17–19 September 2008, pp 95–104
Packalén P, Maltamo M (2008) Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Can J For Res 38(7):1750–1760
Persson A, Holmgren J, Söderman U (2002) Detecting and measuring individual trees using an airborne laser scanner. Photogramm Eng Remote Sens 68(9):925–932
Pitkänen J, Maltamo M, Hyyppä J, Yu X (2004) Adaptive methods for individual tree detection on airborne laser based canopy height model. Int Arch Photogramm Remote Sens Spatial Inf Sci 36(8):187–191
Reynolds MR, Burk TE, Huang W-C (1988) Goodness-of-fit tests and model selection procedures for diameter distribution models. For Sci 34(2):373–399
St-Onge B, Jumelet J, Cobello M, Véga C (2004) Measuring individual tree height using a combination of stereophotogrammetry and lidar. Can J For Res 34(10):2122–2130
Vastaranta M, Wulder MA, White JC, Pekkarinen A, Tuominen S, Ginzler C, Kankare V, Holopainen M, Hyyppä J, Hyyppä H (2013) Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update. Can J Remote Sens 39(5):382–395
Vauhkonen J, Ene L, Gupta S, Heinzel J, Holmgren J, Pitkänen J, Solberg S, Wang Y, Weinacker H, Hauglin KM, Lien V (2012) Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85(1):27–40
Wehr A, Lohr U (1999) Airborne laser scanning—an introduction and overview. ISPRS J Photogramm Remote Sens 54(2):68–82
White JC, Stepper C, Tompalski P, Coops NC, Wulder MA (2015) Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment. Forests 6(10):3704–3732
White JC, Wulder MA, Vastaranta M, Coops NC, Pitt D, Woods M (2013) The utility of image-based point clouds for forest inventory: a comparison with airborne laser scanning. Forests 4(3):518–536
Yu B, Gong P, Pu R (1999) Penalized discriminant analysis of in situ hyperspectral data for conifer species recognition. IEEE Trans Geosci Remote Sens 37(5):2569–2577
Yu X, Hyyppä J, Kukko A, Maltamo M, Kaartinen H (2006) Change detection techniques for canopy height growth measurements using airborne laser scanner data. Photogramm Eng Remote Sens 72(12):1339–1348
Acknowledgments
This study has been conducted with funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement Number 606971, Finnish Cultural Foundation under grant 00150939, and from the Academy of Finland in the form of the Centre of Excellence in Laser Scanning Research (Project Number 272195).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Tanhuanpää, T. et al. (2017). Accuracy of High-Altitude Photogrammetric Point Clouds in Mapping. In: Ivan, I., Singleton, A., Horák, J., Inspektor, T. (eds) The Rise of Big Spatial Data. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-45123-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-45123-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45122-0
Online ISBN: 978-3-319-45123-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)