Comparing airborne and spaceborne photon-counting LiDAR canopy structural estimates across different boreal forest types
Notice bibliographique
Résumé
The monitoring of forested ecosystems relies on an accurate description of forest structure. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2), launched in September 2018, carries the Advanced Topographic Laser Altimeter System (ATLAS), a Light Detection and Ranging (LiDAR) instrument capable of detecting individual photons reflected back from vegetation canopy . ICESat-2 data is delivering global estimates of forest structure; however, analysis of the performance of ICESat-2 on-orbit data across a range of forest conditions remains limited. This study derives structural estimates of (i) canopy height, (ii) canopy cover and (iii) canopy height variability from ICESat-2 data acquired in snow-free and low atmospheric scattering conditions over different boreal forest structural types in Ontario, Canada. ICESat-2 structural estimates were derived from the Global Geolocated Photon Data (ATL03) and Land and Vegetation Height (ATL08) data products and compared against single-photon detection airborne LiDAR (Leica SPL100). An extensive network of ground plots were used to stratify the study area into three distinct forest structural groups, each resulting from different stand development stages. ICESat-2 and SPL100 estimates of canopy height were compared at the ATL03 photon level, whereas estimates of height variability and canopy cover were compared for spatial analysis units (AU; mean size = 1287 m 2 ). ICESat-2 photons returned from the top of the canopy underestimated canopy height relative to SPL100 by an average of 2.3 m overall and corresponded most strongly to the 90th percentile (P90) of coincident airborne SPL100 returns (root mean square difference (RMSD) = 2.9 m and correlation coefficient ( r ) = 0.84). The lowest average underestimation of SPL P90 was observed in homogeneous stands that were relatively simple, and single-layered with a single dominant species (RMSD = 2.5 m, r = 0.84). We observed the least agreement of ICESat-2 and SPL forest structural metrics in over-mature stands with complex structure and greater variability in canopy heights (RMSD = 3.5 m, r = 0.64). For the AUs, the strength of the relationship between SPL100 and ICESat-2 canopy height percentiles increased with increasing height percentiles (e.g. P25 RMSD% = 77.8%; P95 RMSD% = 23.7%). ICESat-2 generally underestimated canopy height variability relative to the SPL100 data, with both data having similar absolute variability (standard deviation of canopy heights RMSD% = 26.8%, r = 0.75), but lower agreement in relative variability (coefficient of variation of canopy heights RMSD% = 33.9%, r = 0.45). Herein we propose the use of the vegetation fill index as a method to estimate canopy cover with ICESat-2. Comparison of SPL100 and ICESat-2 vegetation fill indices at the AU level resulted in strong agreement overall (RMSD% = 19.7%; r = 0.57). These observations and results contribute to the overall objective of building a comprehensive understanding of the performance of ICESat-2 for characterizing vegetation structure in boreal forest environments.
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) | 0,000 | 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 ».