Research article

Forest inventory sensitivity to UAS-based image processing algorithms

Bonifasius Maturbongs, Michael G. Wing , Bogdan Strimbu, Jon Burnett

Bonifasius Maturbongs
College of Forestry, Oregon State University, Corvallis OR USA
Michael G. Wing
College of Forestry, Oregon State University, Corvallis OR USA. Email: michael.wing@oregonstate.edu
Bogdan Strimbu
College of Forestry, Oregon State University, Corvallis OR USA
Jon Burnett
US Forest Service, Pacific Northwest Station, Corvallis OR USA

Online First: July 30, 2019
Maturbongs, B., Wing, M., Strimbu, B., Burnett, J. 2019. Forest inventory sensitivity to UAS-based image processing algorithms. Annals of Forest Research DOI:10.15287/afr.2018.1282


Frequent and accurate estimation of forest structure parameters, such as number of trees per hectare or total height, are mandatory for sustainable forest management. Unmanned aircraft system (UAS) equipped with inexpensive sensors can be used to monitor and measure forest structure. The detailed information provided by the UAS allows tree level forest inventory. However, tree identification depends on a variety of parameters defining the image processing and tree segmentation algorithms. The objective of our study was to identify parameter combinations that accurately delineated trees and their heights. We evaluated the impact of different tree segmentation and point cloud generation algorithms on forest inventory from imagery collected with a UAS over a mature Douglas-fir plantation forest. We processed the images with two commonly used commercial software packages, Agisoft PhotoScan and Pix4Dmapper, both implementing image processing algorithms called Structure from Motion. For each software we generated photogrammetric point clouds by varying the parameters defining the implementation. We segmented individual trees and heights using three tree algorithms: Variable Window Filter, Graph-Theoretical, and Watershed Segmentation. We assessed the impact of image processing algorithms on forest inventory by comparing the estimated trees with trees manually identified from the point clouds. We found that the type of tree segmentation and image processing algorithms have a significant effect in accurately identifying trees. For tree height estimation, we found strong evidence that image processing algorithms had significant effects, whereas tree segmentation algorithms did not significantly affect tree height estimation.These findings may be of interest to others that are using high-resolution spatial imagery to estimate forest inventory parameters.


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  • Bonifasius Maturbongs
  • Michael G. Wing
  • Bogdan Strimbu
  • Jon Burnett
  • Bonifasius Maturbongs
  • Michael G. Wing
  • Bogdan Strimbu
  • Jon Burnett