Estimation of individual tree metrics using structure-from-motion photogrammetry.
Degree GrantorUniversity of Canterbury
Degree NameMaster of Science
The deficiencies of traditional dendrometry mean improvements in methods of tree mensuration are necessary in order to obtain accurate tree metrics for applications such as resource appraisal, and biophysical and ecological modelling. This thesis tests the potential of SfM-MVS (Structure-fromMotion with Multi-View Stereo-photogrammetry) using the software package PhotoScan Professional, for accurately determining linear (2D) and volumetric (3D) tree metrics. SfM is a remote sensing technique, in which the 3D position of objects is calculated from a series of photographs, resulting in a 3D point cloud model. Unlike other photogrammetric techniques, SfM requires no control points or camera calibration. The MVS component of model reconstruction generates a mesh surface based on the structure of the SfM point cloud.
The study was divided into two research components, for which two different groups of study trees were used: 1) 30 small, potted ‘nursery’ trees (mean height 2.98 m), for which exact measurements could be made and field settings could be modified, and; 2) 35 mature ‘landscape’ trees (mean height 8.6 m) located in parks and reserves in urban areas around the South Island, New Zealand, for which field settings could not be modified.
The first component of research tested the ability of SfM-MVS to reconstruct spatially-accurate 3D models from which 2D (height, crown spread, crown depth, stem diameter) and 3D (volume) tree metrics could be estimated. Each of the 30 nursery trees was photographed and measured with traditional dendrometry to obtain ground truth values with which to evaluate against SfM-MVS estimates. The trees were destructively sampled by way of xylometry, in order to obtain true volume values. The RMSE for SfM-MVS estimates of linear tree metrics ranged between 2.6% and 20.7%, and between 12.3% and 47.5% for volumetric tree metrics. Tree stems were reconstructed very well though slender stems and branches were reconstructed poorly.
The second component of research tested the ability of SfM-MVS to reconstruct spatially-accurate 3D models from which height and DBH could be estimated. Each of the 35 landscape trees, which varied in height and species, were photographed, and ground truth values were obtained to evaluate against SfM-MVS estimates. As well as this, each photoset was thinned to find the minimum number of images required to achieve total image alignment in PhotoScan and produce an SfM point cloud (minimum photoset), from which 2D metrics could be estimated. The height and DBH were estimated by SfM-MVS from the complete photosets with RMSE of 6.2% and 5.6% respectively. The height and DBH were estimated from the minimum photosets with RMSE of 9.3% and 7.4% respectively. The minimum number of images required to achieve total alignment was between 20 and 50. There does not appear to be a correlation between the minimum number of images required for alignment and the error in the estimates of height or DBH (R2 =0.001 and 0.09 respectively). Tree height does not appear to affect the minimum number of images required for image alignment (R 2 =0.08).