Assessment of remotely piloted aircraft data classification of wetland vegetation communities and changes in their pattern with elevation
Notice bibliographique
Résumé
Wetlands host a myriad of services both to the environment and society. While the provision of those services are partly dependent on the terrestrial plant communities that both comprise and are adjacent to a wetland, little has been done to map these communities at high spatial resolutions or measure their composition and configuration. Remotely piloted aircraft (RPA) have been used to capture very-high resolution imagery that can aid our understanding of wetland characteristics and function without harming the local environment. However, a gap remains in our understanding of the composition and configuration of vegetation communities in high-elevation wetlands. As a step toward improving our understanding, an RPA was used to collect multi-spectral data (red edge, green, blue, near infrared) and analyzed to determine the ability to identify and map the composition and configuration of wetland vegetation communities at high elevations in Alberta, Canada. These wetlands have been excluded from the Canadian Wetland Classification System mapped areas. In addition to assessing the ability for automated classification of vegetation communities in an object-based image analysis, the relative contribution each spectral band, their combination in different indices (e.g., NDVI), and a generated digital surface model had on classification accuracy was quantified. Results from a random tree classifier obtained an overall accuracy of 91.43%, total producer’s accuracy of 86.9% and total user’s accuracy of 92.7%. Of 17 inputs (12 image layers and 5 objects features) included in the classifier, the Digital Surface Model had the greatest overall importance (average of 15.9%). While comparison between the classifier and testing samples derived through manual segmentation selection yielded high accuracies, comparison of the automated classification against ground survey plots were substantially less accurate (total overall accuracy 54.5%). Analysis of the composition and configuration of the classified RPA data identified five non-correlated landscape metrics that showed statistically significant differences when wetlands were compared across a gradient in elevation. These results demonstrate that elevation can affect the pattern of wetland vegetation and that further research should be done to determine if and how these pattern changes may affect specific wetland functions. In addition to situating the aforementioned results among the broader literature, the potential for improved vegetation mapping with remotely piloted aircraft is discussed along with the need for a standard set of reporting variables for RPA data collection to facilitate comparative analyses.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».