A remote sensing-based strategy for mapping anthropogenic urban surface ecological poorness zones (AUSEPZ): A case study of Lisbon City
Bibliographic record
Abstract
Anthropogenic activities play a crucial role in the formation and intensification of Urban Surface Ecological Poorness Zones (USEPZ). This study introduces a methodology for assessing the spatiotemporal fluctuations of Anthropogenic USEPZ (AUSEPZ), using Lisbon city and the Setúbal district as a case study to demonstrate its effectiveness. By integrating data from various surface characteristics through the Comprehensive Ecological Evaluation Index (CEEI), Surface Ecological Condition (SEC) maps were developed, and their spatial and temporal variations were analyzed. Additionally, a feature space was established between the Impervious Surface Percentage (ISP) and CEEI to calculate AUSEPZ intensity across different years. The findings revealed that the mean CEEI of Lisbon increased by 0.41 between 1986 and 2023. During this period, the proportions of SEC classified as Excellent, Very Good, Good, Fair, and Poor changed by −52 %, −13 %, +107 %, +444 %, and + 1134 %, respectively. The AUSEPZ intensity values for Lisbon were 0.32, 0.39, 0.46, 0.52, 0.57, and 0.63 for the years 1986, 1994, 2001, 2008, 2015, and 2023, respectively. The intensification of human activities, driven by urban expansion and population growth, has significantly contributed to the deterioration of SEC in Lisbon over recent years. These findings provide valuable insights for urban planners, policymakers, and stakeholders, enabling the design of targeted strategies to mitigate the impacts of urbanization and enhance ecological conditions in urban areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".