Accuracy of High-Altitude Photogrammetric Point Clouds in Mapping

  • Topi Tanhuanpää
  • Ninni Saarinen
  • Ville Kankare
  • Kimmo Nurminen
  • Mikko Vastaranta
  • Eija Honkavaara
  • Mika Karjalainen
  • Xiaowei Yu
  • Markus Holopainen
  • Juha Hyyppä
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

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.

Keywords

Image-based point clouds LiDAR Height models Tree detection Forest 

Notes

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).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Topi Tanhuanpää
    • 1
    • 3
  • Ninni Saarinen
    • 1
    • 3
  • Ville Kankare
    • 1
    • 3
  • Kimmo Nurminen
    • 2
  • Mikko Vastaranta
    • 1
    • 3
  • Eija Honkavaara
    • 2
  • Mika Karjalainen
    • 2
    • 3
  • Xiaowei Yu
    • 2
    • 3
  • Markus Holopainen
    • 1
    • 3
  • Juha Hyyppä
    • 2
    • 3
  1. 1.Department of Forest SciencesUniversity of HelsinkiHelsinkiFinland
  2. 2.National Land SurveyFinnish Geospatial Research InstituteMasalaFinland
  3. 3.Centre of Excellence in Laser Scanning ResearchFinnish Geospatial Research InstituteMasalaFinland

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