URBAN HEAT ISLAND FOOTPRINT EFFECTS ON BIO-PRODUCTIVE RURAL LAND COVERS SURROUNDING A LOW DENSITY URBAN CENTER
Bibliographic record
Abstract
Abstract. The urban heat island (UHI) is a common effect caused by urbanization and has been studied to evaluate the thermal condition in cities worldwide. However, most previous UHI analyses are performed in major metropolitan cities. This study conducts a spatiotemporal analysis of UHI in a rapidly expanding low-density suburban centre and determines how bio-productive land covers react and the extent of the disturbance to each land cover based on time series land surface temperatures extracted from Landsat 7 ETM+ images. Two methods applied and compared are the single exponential decay method, which measures UHI footprint (UHIFP) on vegetation phenology, and the two dimensional Gaussian surface, which quantifies the influence based on distance from the local urban perimeter. Three spectral indices (Normalized Difference Vegetation Index (NDVI), Moisture Index (NDMI), and the Enhanced Vegetation Index (EVI)) were extracted and the residuals from the Gaussian model were compared based on these indices in order to better understand the thermal variations of each land cover within a UHI. The results show that the UHIFP of the studied low-density suburban centre is 1.4 times larger than the size of the urban centre, marginally smaller than previous analyses performed within high-density metropolises. All vegetated land covers experienced their maximum cooling effects before reaching the UHIFP perimeter while urban surfaces begin to diverge from the Gaussian model outside of the UHIFP. The residuals of sparse vegetation maintained strong correlations with each index throughout the growing season while NDMI retained the strongest relationships with every land cover. This study has helped us better understand the UHI effects of small communities with varied vegetation phonology based on the distribution of built-up pervious and impervious surfaces within the neighbourhood structure. The similar results from both methods indicate a strong urban cover influence overpowering the dominant distribution of agricultural surfaces throughout the growing season.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".