Moderate-resolution mapping of aboveground biomass stocks, forest structure, and composition in coastal Alaska and British Columbia
Notice bibliographique
Résumé
The forests of coastal Alaska and British Columbia are globally significant for their high carbon storage capacity and complex forest structure, hosting some of the densest values of aboveground biomass in the world. These ecosystems support biodiversity, provide critical habitat, and serve as long-term carbon sinks, offering resilience to climate change. However, comprehensive, spatially continuous estimates of forest structure across this region have been limited, particularly across political boundaries. In this study, we used a Gradient Nearest Neighbor (GNN) modeling approach to integrate extensive forest inventory plot data with satellite-derived environmental variables. This approach enabled us to produce high-resolution (30-meter) maps of aboveground biomass, species-specific biomass, forest age, basal area, and additional structural attributes. Our results indicate that climate and topography accounted for the majority of the explainable variation across all modeling regions. Predictions of aboveground live biomass were higher than previous estimates, particularly in Southeast Alaska, where estimates were 30-53% greater than previous studies. Forest structure varied across the region, with older forests found in Southeast Alaska and higher tree densities in British Columbia. Collectively, the coastal forests of Alaska and British Columbia store approximately 3.58 petagrams of carbon. These spatially explicit maps offer critical insights for forest management, carbon monitoring, and biodiversity conservation across this ecologically diverse and politically fragmented landscape. Citations for Forest Inventory and Analysis: Burrill, Elizabeth A.; DiTommaso, Andrea M.; Turner, Jeffery A.; Pugh, Scott A.; Christensen, Glenn; Perry, Carol J.; Lepine, Lucie C.; Walker, David M.; Conkling, Barbara L. 2024. The Forest Inventory and Analysis Database, FIADB user guides, volume database description (version 9.2), nationwide forest inventory (NFI). U.S. Department of Agriculture, Forest Service. 1042 p. [Online]. Available at web address: https://www.fs.usda.gov/research/products/dataandtools/datasets/fia-datamart Citation for Forest Analysis and Inventory Branch: Bourgeois, William; Brinley, Clark; LeMay, Valerie; Moss, Ian; Reynolds, Nick. 2018. “British Columbia Forest Inventory Review Panel Technical Background Report.” Office of the Chief Forester Division, British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development, December 2018. https://www2.gov.bc.ca/assets/gov/farming-natural-resources-and-industry/forestry/stewardship/forest-analysis-inventory/brp_technical_document_final.pdf. Distribution Liability: The USDA Forest Service manages resource information and derived data as a service to users of USDA Forest Service digital geographic data. The USDA Forest Service is in no way condoning or endorsing the application of these data for any given purpose. It is the sole responsibility of the user to determine whether or not the data are suitable for the intended purpose. It is also the obligation of the user to apply those data in an appropriate and conscientious manner. The USDA Forest Service provides no warranty, nor accepts any liability occurring from any incorrect, incomplete, or misleading data, or from any incorrect, incomplete, or misleading use of these data. Use Constraints: The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.
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 | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».