Dynamics of Impervious Surfaces and Vegetation in Core Urban Areas of Global Megacities
Bibliographic record
Abstract
Although many studies have shown that the expansion of impervious surfaces such as artificial buildings and roads is accompanied by a decrease in urban vegetation, recent studies have also pointed out that urban greening measures can promote vegetation growth. The above studies mostly focus on administrative regions such as a certain city, urban agglomeration, river basin, country, etc., ignoring the differences between suburbs and core urban areas with concentrated populations. This paper proposes fractional impervious surface index (FISI) to estimate impervious surfaces and combines it with population information to define core urban area boundaries of megacities. Then, Landsat data are used to analyze dynamics of impervious surfaces and vegetation in core urban areas of 12 megacities around the world from 2013 to 2022. The results indicate a gradual increase in impervious surfaces within the core urban areas of 12 megacities. Most megacities have shown a significant decline in growth rate of impervious surfaces since 2017, and growth rates of all megacities have tended to stabilize after 2019. Across all megacities, lower fractional vegetation cover (FVC) levels account for a higher proportion of area, with a gradual decline toward higher FVC levels. Most megacities have different FVC trends in core urban areas and entire city. The proportions of very high FVC areas in core urban areas of 12 megacities have not changed much, with fluctuations basically not exceeding 5%. This study helps to understand the spatiotemporal dynamic changes of vegetation in core urban areas and provide reference data for sustainable urban development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".