The Disappearing Green: Ecosystem Service Loss in Atlanta, Georgia
Bibliographic record
Abstract
Urban green spaces play a critical regulatory role in sustaining ecosystem functions, however, rapid urbanization has significantly altered land-use patterns, reduced green cover, and intensified both urban heat island effects and air pollution in many cities around the world. This study assesses the spatiotemporal land-use/land-cover (LULC) changes in Atlanta, Georgia, and examines their effects on ecosystem services, urban heat island (UHI) intensity, and air-quality conditions. Landsat imagery from 1995, 2005, 2015, and 2025 was processed using Support Vector Machine (SVM) classification to quantify LULC transitions, while NDVI-based emissivity land surface temperature (LST) retrieval and interpolated PM2.5 concentrations were used to evaluate thermal and air-quality patterns. Results indicate substantial ecosystem service depletion, with forest cover declining by 19.94% and water bodies by 65.17%, while developed land increased by 83.46% between 1995 and 2025. UHI analysis for 2025 showed temperatures ranging from 21.8°C in vegetated zones to 38.4°C in highly urbanized areas, representing a relative UHI intensity of 16.6°C. Air-quality patterns revealed similarly concerning trends, with PM2.5 concentrations rising to 18–22 µg/m³ in built-up areas over 40% higher than levels in undeveloped areas. The study identifies uncontrolled population growth, urban sprawl, and infrastructure expansion as key drivers of these changes and recommends promoting infill development, expanding green infrastructure, and integrating nature-based solutions such as green roofs and bioswales in future urban planning.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".