Forest data collection using terrestrial image-based point clouds from a handheld camera compared to terrestrial and personal laser scanning

Xinlian Liang, Yunsheng Wang, Anttoni Jaakkola, Antero Kukko, Harri Kaartinen, Juha Hyyppä, Eija Honkavaara, Jingbin Liu

Research output: Contribution to journalArticleScientificpeer-review

45 Citations (Scopus)

Abstract

Stereo images have long been the main practical data source for the high-accuracy retrieval of 3-D information over large areas. However, stereoscopy has been surpassed by laser scanning (LS) techniques in recent years, particularly in forested areas, because the reflection of laser points from object surfaces directly provides 3-D geometric features and because the laser beam has good penetration capacity through forest canopies. In the last few years, image-based point clouds have become a more widely available data source because of advances in matching algorithms and computer hardware. This paper explores the possibility of using consumer cameras for forest field data collection and presents an application of terrestrial image-based point clouds derived from a handheld camera to forest plot inventories. In the experiment, the sample forest plot was photographed in a stop-and-go mode using different routes and camera settings. Five data sets were generated from photographs taken in the field, representing different photographic conditions. The stem detection accuracy ranged between 60% and 84%, and the root-mean-square errors of the estimated diameters at breast height were between 2.98 and 6.79 cm. The performance of image-based point clouds in forest data collection was compared with that of point clouds derived from two LS techniques, i.e., terrestrial LS (the professional level) and personal LS (an emerging technology). The study indicates that the construction of image-based point clouds of forest field data requires only low-cost, low-weight, and easy-to-use equipment and automated data processing. Photographic measurement is easy and relatively fast. The accuracy of tree attribute estimates is close to an acceptable level for forest field inventory but is lower than that achieved with the tested LS techniques.

Original languageEnglish
Article number7109840
Pages (from-to)5117-5132
Number of pages16
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume53
Issue number9
DOIs
Publication statusPublished - 1 Sep 2015
MoE publication typeA1 Journal article-refereed

Keywords

  • Forest inventory
  • handheld camera
  • image-based point cloud
  • laser scanning (LS)
  • Light Detection And Ranging (LiDAR)
  • point cloud
  • structure from motion
  • terrestrial

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