Assessment of Ecological Quality Changes of Vegetation in Fuzhou City from 2000 to 2020 Based on MODIS Observations
Bibliographic record
Abstract
As an advocate of "urban ecology construction" and the capital of Fujian, an "ecological province", the ecological quality of vegetation in Fuzhou City is crucial to the development of ecological civilization in the region.The ecological quality of vegetation in Fuzhou City was assessed by quantifying the distribution and changes in the Normalized Difference Vegetation Index (NDVI) and the Net Primary Productivity (NPP) of vegetation from 2000 to 2020 and exploring the influence of urban development on their distribution.The result shows that the multi-year average Vegetation Fraction Cover (VFC) of the region is 68%, showing a significant growth trend (0.32% yr -1 ), reaching 72.43% by 2020.The multi-year average NPP is 14.6 × 10 3 kg C•m -2 •yr -1 , and the vegetation carbon sequestration showed a smooth fluctuation in the last two decades.VFC and NPP are relatively low on the eastern coastal and riverine areas, while they had higher levels in the inland mountainous areas.However, the ecological quality of vegetation in urban areas is poor and showing a decreasing trend, for the effect from urbanization.The construction of ecological civilization has led to an upward trend in the overall ecological quality of vegetation in Fuzhou City, but regional economic development has caused a decline in the ecological quality of vegetation in urban areas and surrounding areas.This study provides a basis for further regional vegetation ecology research and also has important implications for balancing urban development and vegetation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".