Development of Advanced Remote Sensing Methods in Quantifying Wildlife Habitat Management
Notice bibliographique
Résumé
Wildlife habitats have been affected by human activities and climate change. Animal diversity is declining at an unprecedented rate. Tools used to obtain a rapid assessment of wildlife habitats at different scales are urgently needed. The habitat management tools that are currently used for conservation and monitoring wildlife are often limited by the availability of mapped habitat information that is tailored to the wildlife of interest and that covers appropriate geographic and temporal extents of interest. Failure to adequately map specific habitat features can limit effective management. Advancements in remote sensing and related technologies have increased the resolution and quantity of landscape data, providing an excellent opportunity to extract various environmental features for examining habitat selection and mapping wildlife habitats to a broad extent. \nTo exploit the potential of the emergent remote sensing data sets, the focus of this study was to develop advanced methodologies to derive information related to the properties of environmental features at different scales and to generate tools to improve the understanding of a wildlife habitat landscape that can benefit from habitat management. Specifically, an advanced algorithm was developed that utilized spatial pattern analysis to classify the forest succession stages from optical imagery and had a classification accuracy of 89%. In addition, a novel method was proposed to extract road features from the road structure knowledge followed by a deep learning VGG 16 classification for a refined output. An overall accuracy of 74% was achieved for the forest road extraction. A robust and operational stepwise automatic thresholding method was developed to accurately map the dynamics of surface water bodies from SAR data, with an overall accuracy of 95%. In addition, an advanced fuzzy AHP model was utilized to accurately map beaver-altered wetlands in the landscape using remote sensing products derived based on the knowledge of beaver activities, where an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds were correctly identified. In conclusion, this research demonstrated that the advanced methods utilizing multi-source and multi-temporal remote sensing data could effectively characterize and extract environmental features that benefit wildlife habitat management.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».