Terrestrial structure from motion photogrammetry for deriving forest inventory data

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Livia Piermattei
  • Wilfried Karel
  • Di Wang

  • Martin Wieser
  • Martin Mokroš
  • Peter Surový
  • Milan Koreň
  • Julián Tomaštík
  • Norbert Pfeifer
  • Markus Hollaus

Research units

  • Vienna University of Technology
  • Catholic University of Eichstätt-Ingolstadt
  • Czech University of Life Sciences Prague
  • Technical University in Zvolen

Abstract

The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TUWien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is -1.13 cm and -0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum -2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

Details

Original languageEnglish
Article number950
Number of pages23
JournalRemote Sensing
Volume11
Issue number8
Publication statusPublished - 1 Apr 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Diameter at breast height, Plot-based forest inventory, Structure from motion, Terrestrial laser scanning, Terrestrial photogrammetry, plot-based forest inventory, terrestrial photogrammetry, HEIGHT, ACCURACY, terrestrial laser scanning, DIAMETER, structure from motion, MODELS, BIOMASS, HAND-HELD CAMERA, diameter at breast height, LASER SCANNER

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