LiDAR-based Ecosystem Classification for Calvert Island
Notice bibliographique
Résumé
The purpose of this work was to define and map a set of repeating ecohydrological classes on Calvert and Hecate Islands using remote sensing data and an unsupervised classification technique. The resulting map provides a new tool for characterizing the extent and internal properties of different ecosystem classes, for stratifying future study designs, and for evaluating the influence of terrestrial landscape characteristics on watershed processes. "Traditionally, forest inventory and ecosystem mapping at local to regional scales rely on manual interpretation of aerial photographs, based on standardized, expert-driven classification schemes. These current approaches provide the information needed for forest ecosystem management but constrain the thematic and spatial resolution of mapping and are infrequently repeated. The goal of this research was to demonstrate the utility of an unsupervised, quantitative technique based on Light Detection And Ranging (LiDAR) data and multi-spectral satellite imagery for mapping local-scale ecosystems over a heterogeneous landscape of forested and non-forested ecosystems. We derived a range of metrics characterizing local terrain and vegetation from LiDAR and RapidEye imagery for Calvert and Hecate Islands, British Columbia. These metrics were used in a cluster analysis to classify and quantitatively characterize ecological units across the island. A total of 18 clusters were derived. The clusters were attributed with quantitative summary statistics from the remotely sensed data inputs and contextualized through comparison to ecological units delineated in a traditional expert-driven mapping method using aerial photographs. The 18 clusters describe ecosystems ranging from open shrublands to dense, productive forest and include a riparian zone and many wetter and wetland ecosystems. The clusters provide detailed, spatially-explicit information for characterizing the landscape as a mosaic of units defined by topography and vegetation structure. This study demonstrates that using various types of remotely sensed data in a quantitative classification can provide scientists and managers with multi- variate information unique from that which results from traditional, expert-based ecosystem mapping methods." - Abstract from Thompson et al. 2016. A complete explanation of methods is available in Thompson et al. 2016. A small number of data voids in the 2012 LiDAR coverage were present and were excluded from the analysis. Although the voids have since been filled with new LiDAR data acquired in 2014, the new data were not included in the analysis of Thompson et al. Other “gaps” in the spatial coverage of the final map are a result of the exclusion of non-vegetated areas (as guided by the Normalized Difference Vegetation Index (NDVI) and the provincial Freshwater Atlas (FWA): https://www2.gov.bc.ca/gov/content/data/geographic-data-services/topographic-data/freshwater). In addition to small waterbodies, these non-vegetated areas include a few small areas at high elevation that were snow-covered at the time of the RapidEye image acquisition.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 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) | 0,001 | 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 ».