Spectral separability of riparian forests from small and medium-sized rivers across a latitudinal gradient using multispectral imagery
Notice bibliographique
Résumé
Spectral discrimination between riparian forests is a challenging issue due to the inherent complexity of species composition and the high spatial structural variability of these vegetation types. This study aimed to evaluate spectral separability among riparian forests, in small and medium-sized river catchment areas, in three bioclimatic zones of Portugal (temperate, transitional, and Mediterranean). We also assess the spectral differences using only the dominant riparian woody species in each riparian forest class, namely Alnus glutinosa, Salix salviifolia, and Nerium oleander. Pixel values were extracted from high-resolution airborne multispectral imagery (red, green, blue, and near-infrared, 50 cm pixels) of 26 riparian forests located in the three bioclimatic zones. Spectral separability was calculated using the transformed divergence (TD) distance. Discriminant analysis (DA) was used to select the bands that contribute most to the spectral separability and for the classification accuracy assessment of the riparian forests. Species composition and percentage of canopy closure were collected for all the riparian forests in a field campaign and subjected to hierarchical clustering in order to validate the spectral separability analyses. Optical traits derived from field data were used to interpret the spectral differences between riparian forest classes. The greatest spectral separability was observed between the temperate and the Mediterranean riparian forest classes. Global classification accuracy for the DA was 86.3% for riparian forest classes along medium-sized rivers and 70.1% in small-sized ones. The high floristic and spatial structure variability was responsible for the misclassification errors that occurred between the transitional and the other riparian forest classes. The spectral separability using only the dominant species was greater than that obtained using the overall species assemblages of the riparian forests. Alnus glutinosa had the highest level of classification accuracy, and this may be related to its peculiar yellowish-green tone. DA also revealed that all spectral bands were needed in order to distinguish the riparian forest classes. This study provided evidence that the spectral discrimination of riparian forests can be explained on the basis of differences in species composition and cover, and by a convergence of optical traits, at both leaf and canopy levels. Spectral signatures of these riparian forests and related spectral signatures of key species are useful tools for evaluating the floristic deviations of actual riparian forests from their near-natural benchmarks.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 tête enseignante, 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 ».