Evaluating ICESat-2 for monitoring, modeling, and update of large area forest canopy height products
Notice bibliographique
Résumé
Forests represent the world's largest terrestrial ecosystem and their monitoring is therefore critical from scientific, ecological, and management perspectives. Present day sustainable forest management practices go beyond forest inventory and increasingly include aspects such as carbon accounting and regeneration assessments. Such monitoring requires often unavailable, spatially exhaustive and up-to-date information on forest attributes over broad areas. Recent developments in the acquisition of broad-scale forest attribute information from remotely sensed data has included the use of multiple technologies that take advantage of globally available data products to derive forest attribute layers. However, less is known about the applicability and performance of such products when used to produce broad-scale, accurate, and up-to-date forest information products. This study aimed to evaluate the agreement between two broad-scale forest canopy height products – Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) and the National Terrestrial Ecosystem Monitoring System (NTEMS) imputed canopy height layers for Canada – across a variety of ecological gradients. Overall, the two datasets showed high correspondence, with a root-mean-square difference of 4.87 m, and 85% of ICESat-2 canopy heights falling within the 95% confidence interval of the NTEMS height estimate. Across ecozones, canopy heights in the Taiga Shield West and Boreal Shield West had stronger agreement (91% of ICESat-2 segments within the 95% confidence interval of NTEMS), while the Taiga Cordillera and Taiga Shield East had lower agreement (< 75% of ICESat-2 segments within the 95% confidence interval of NTEMS). Interestingly, we found that the modeled heights based upon optical satellite data had a less generalized distribution than heights from ICESat-2 as well as achieving a greater representation for the taller (overall and by ecozone) height classes. An increase in absolute difference between data products was also found as a function of increasing slope. Finally, the correspondence between products was evaluated across disturbed areas (35 to 10 years since disturbance) to assess the agreement of the two products in areas of regenerating forest. In general, the analysis found that burned areas, which tend to be more structurally heterogeneous, had lower agreement between products then harvested areas. The high overall correspondence between the data products demonstrate the potential for integration of ICESat-2 to inform (via calibration / validation) or update height products based upon optical satellite data.
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 ».