The accuracy of deflection-lines derived from digital elevation models
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».