The accuracy of deflection-lines derived from digital elevation models
Bibliographic record
Abstract
Long-reaching skyline cable yarding systems have seen increased use within the British Columbia coastal forest industry. Deflection-line analysis, which estimates the maximum yarding distance and locates the harvest boundary, is the key component in planning for skyline systems. Traditional deflection-line analysis involves field surveys which may be very difficult to perform in the terrain associated with skylines. As an alternative, deflection-lines may be derived from Digital Elevation Models (DEMs). Concern regarding the elevational accuracy of the topographic forest planning maps used to create the DEMs has limited their use for deflection-line analysis. Better understanding of the magnitude and nature of elevational errors and their effect upon deflection-line analysis are needed before DEM-derived deflection-lines may be used with confidence. This study was performed in cooperation with Canadian Forest Products Limited (Canfor) in Woss, British Columbia (B.C.). Deflection-line analyses were performed for DEM-derived deflection-lines to test for error in yarding distance estimates. Errors in yarding distance estimates for DEM-derived deflection-lines were caused by interactions between some or all of the following: the terrain shape (concavity/convexity), large elevational errors and their location on the deflection-line, and the deflection-line length. While a majority of yarding distance estimates from DEM-derived deflection-lines were not in error (70%), the erroneous estimates may result in costly planning errors. Restricting the use of DEM-derived deflection-lines to the efficient pre-planning of field surveys could help avoid these mistakes. A blunder was detected in one of the study cutblock maps. Distortions were discovered in the maps for two other study cutbiocks where photogrammetrically derived and ground surveyed maps had been joined through rubber sheeting. While random error was detected in the analyses, systematic error appeared to contribute more to both the general level of elevational error and to the presence of large elevational errors. Different types of systematic error were detected, with at least some types evident in all of the deflection-line comparisons. Smoothing error was observed where terrain variation had been reduced or eliminated, and positional errors were the most common and influential systematic errors detected. The positional error of map features, and positional error introduced using traditional surveying methods, may also affect operational field surveying of deflection-lines, logging roads, and harvest boundaries. The presence of positional error and its subsequent effects upon harvest planning is either not known or is ignored altogether. Detecting the presence of systematic error in topographic forest planning maps is the first step towards using DEMs confidently for deflection-line analysis. Further studies involving the effects of positional error on DEM elevational error will allow the DEMs to be predicted and subsequently accounted for. Advances in map creation, computers, and Geographic Information Systems will allow for the acquisition and manipulation of more accurate digital elevation data now and in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".