Name
Title | ||
|---|---|---|
Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters. |
Abstract | ||
|---|---|---|
The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For example, point clouds from CRP are similar to TLS, but hardware costs are significantly lower. However, a number of data collection issues need to be clarified before the use of CRP in forested areas is considered effective. In this paper we focused on different CRP data collection methods to estimate DBH. We present seven methods that differ in camera orientation, shooting mode, data collection path, and other important factors. The methods were tested on a research plot comprised of European beeches (Fagus sylvatica L.). The circle-fitting algorithm was used to estimate DBH. Four of the seven methods were capable of producing a dense point cloud. The tree detection rate varied from 49% to 81%. Estimates of DBH produced a root mean square error that varied from 4.41 cm to 5.98 cm. The most accurate method was achieved using a vertical camera orientation, stop-and-go shooting mode, and a path leading around the plot with two diagonal paths through the plot. This method also had the highest rate of tree detection (81%). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/ijgi7030093 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
close-range photogrammetry,diameter at breast height,point cloud,circle fitting,forestry | Close range photogrammetry,Data collection,Photogrammetry,Remote sensing,Camera orientation,Mean squared error,Fagus sylvatica,Diameter at breast height,Point cloud,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 3 | 2220-9964 |
Citations | PageRank | References |
2 | 0.43 | 9 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Mokros | 1 | 2 | 0.43 |
| Xinlian Liang | 2 | 193 | 23.72 |
| Peter Surový | 3 | 3 | 1.16 |
| Peter Valent | 4 | 4 | 0.80 |
| Juraj Cernava | 5 | 2 | 0.77 |
| Frantisek Chudý | 6 | 4 | 0.82 |
| Daniel Tunák | 7 | 4 | 0.82 |
| Simon Salon | 8 | 4 | 0.82 |
| Jan Merganic | 9 | 2 | 1.11 |