Emerging trends in global forest above-ground biomass derived from a decadal time record of high-resolution satellite observations
Notice bibliographique
Résumé
Characterization and quantification of terrestrial global carbon stocks increasingly integrates spaceborne remote sensing data. The wide range of multi-decadal time series of temporally consistent observations is key to understanding global processes related to terrestrial vegetation. As the organic mass stored in vegetation cannot be sensed, uncertainties in estimates of carbon stocks from remote sensing data can only be reduced by complementing multiple observations. This aspect is even more crucial given the weak sensitivity of the signals acquired by most systems in space to vegetation structural parameters. High-resolution observations, in addition, better allow natural events (e.g., fires) or processes (e.g., forest dieback) or human activities (e.g., shifting cultivation) to be identified, which in turn, improves understanding and explanations of vegetation carbon dynamics. Time series of above-ground biomass (AGB, live) maps by the European Space Agency (ESA) Climate Change Initiative (CCI) Biomass for the years 2010, 2017, 2018 and 2020 were obtained from a combination of freely available high-resolution C- and L-band radar, laser and optical satellite observations. The pixel size of the maps was set at 100 m as a compromise between preserving spatial detail and reducing observational noise. The non-uniform temporal spacing of the mapping was selected to provide a first assessment of short- and long-term AGB dynamics. Independent assessment based on in situ plots distributed across the major forest biomes and LiDAR-derived maps of AGB indicated a reliable representation of forest AGB levels globally. This first version of the CCI Biomass maps is therefore sufficiently reliable for identifying areas of changes that impact on the terrestrial carbon cycle. On the contrary, the presence of local biases and a 30-40% uncertainty relative to the estimated value do not allow for an estimate of AGB dynamics at the level of individual pixels. Such errors can be attributed to data quality or approximations in the models relating AGB to the remote sensing data. Coupling biomass and land cover CCI data products revealed that, between 2010 and 2020, the terrestrial AGB pool in forests fluctuated between 550 Pg and 560 Pg while the global forest area increased by 1%. AGB changes were particularly evident in areas associated with persistent forest land, with areas such as western Canada and northeast Europe losing biomass whilst others (e.g., central and southeast Asia) experienced net gains. Differentiation between natural and plantation forests was not achieved and so the relative contribution of losses and gains associated with each was not able to be discerned. Conversion of forest to cropland and grassland resulted in a loss of approximately 0.8 Pg of AGB whilst conversion of these non-forest landscapes to forest increased the global AGB pool by 0.2 Pg. This presentation will review these emerging trends and give a perspective on the future suite of ESA CCI Biomass data products, which foresee a more regular temporal sampling, an extended time interval, and the inclusion of a wider range of recent satellite observations (e.g., by spaceborne LiDAR) and satellite data products with improved radiometric quality.
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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 | 0,002 |
| 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) | 0,001 | 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 ».