A Review of Research and Approaches to Measuring Mountain Green Cover, in Alignment with Sustainable Development Goal Indicator 15.4.2
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".