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Intercomparison of photogrammetry software for three-dimensional vegetation modelling.

Alexandra Probst1, Demetrios Gatziolis2, Nikolay Strigul3

  • 1Department of Biology, University of Washington, Seattle, WA, USA.

Royal Society Open Science
|August 16, 2018
PubMed
Summary
This summary is machine-generated.

This study compares photogrammetry algorithms for vegetation assessment. Several methods showed similar reconstruction artifacts, impacting forest inventory accuracy.

Keywords:
forest modellingphotogrammetryremote sensingsimulationtree crown geometryvegetation three-dimensionalre constructions

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Area of Science:

  • Forestry and Remote Sensing
  • Computer Vision and Photogrammetry

Background:

  • Photogrammetry enables 3D reconstruction, with growing applications beyond computer vision.
  • Accurate forest inventory relies on precise field measurements, which photogrammetry can enhance.

Purpose of the Study:

  • To evaluate and compare the performance of various photogrammetry algorithms for vegetation assessment.
  • To determine the suitability of different software implementations for forest inventory applications.

Main Methods:

  • Comparison of photogrammetric algorithms: CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE, and Agisoft PhotoScan.
  • Evaluation using a virtual scene with known object dimensions for quantitative analysis.
  • Assessment using in situ acquired vegetation photographs for qualitative comparison, with ROC curves for error quantification.

Main Results:

  • All tested photogrammetry algorithms exhibited similar types of artifacts in both synthetic and real-world vegetation reconstructions.
  • Receiver operating characteristic (ROC) curves were used to quantify type-I and type-II errors in tree model reconstructions.

Conclusions:

  • The performance of different photogrammetry software varies, but common reconstruction artifacts are observed across methods.
  • Further research is needed to refine photogrammetric techniques for reliable vegetation assessment and forest inventory.