A Review of Research and Approaches to Measuring Mountain Green Cover, in Alignment with Sustainable Development Goal Indicator 15.4.2
Notice bibliographique
Résumé
The United Nations Sustainable Development Goals have three mountain related targets. Of these, target 15.4 is exclusively focused on conserving the mountain ecosystem and measures to evaluate and protect global mountain regions. The Mountain Partnership Secretariat at the Food and Agriculture Organization (FAO) of the United Nations is noted to be the custodian agency of target 15.4 (FAO, 2015). As part of this process, the FAO has developed an indicator, the Mountain Green Cover Index, to monitor progress towards the achievement of this target. The Mountain Green Cover Index is intended to be a quantification mechanism to evaluate variations of green vegetation in mountain areas globally, at varied scales. The index builds on the recognition of a positive correlation between green coverage of mountain areas, their state of health and capacity to fulfil their ecosystem roles (FAO, 2015). Researchers and academics globally have now started evaluating the process of generating a Mountain Green Cover Index. This encompasses a variation of methodologies, technologies and resources, at different geographic scales. These variations exist due to the lack of a uniform methodology that can be applied at all geographic scales. Thus, current methods are faced with several limitations in measuring the Mountain Green Cover Index. This review evaluates the various methods, tools and resources associated with generating a Mountain Green Cover Index, at different geographic scales, by following the systematic literature review approach. As part of this process, various definitions of mountain regions were evaluated, along with the understanding of what regional factors are associated with evaluating such classification areas. Articles from six different databases over the past five years were studied, with fifteen articles being chosen for the systematic review. The eligibility criteria were specific to studies that conducted analysis on measuring regional vegetation and mountain changes, as well as those that evaluated specific components in relation to indicator 15.4.2. These studies included research from different geographic scales (both regional and global) and with data from different time scales. The results showed that while there have been many technological advancements in the area of evaluating land cover change, there are still several limitations associated with understanding mountain green cover at various geographic measures. Technology, however, was also noted to be one of the key components to facilitate the generation of a Mountain Green Cover Index in the studies reviewed. Many studies concluded that implementing a monitoring system for vegetation and mountain changes will require an agile and integrated global innovation system, which allows regions to be connected globally, and facilitate an interactive model for research and knowledge sharing.
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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,012 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».