THE INFLUENCE OF URBAN HEAT ISLAND EFFECT AND ITS RELATIONSHIP WITH LAND COVER, SCALE, AND SEASONALITY IN A LOW-DENSITY URBAN CENTER
Bibliographic record
Abstract
Land surface temperature (LST) and air temperature (Tair) are the primary metrics applied to measure and analyze urban heat island (UHI) effects, a thermal phenomenon caused by urbanization. This thesis aims to study the UHI effect of a rapidly expanding low-density urban center using two satellite based LST products. Milton, Ontario, Canada was selected as the study site due to its rapid urban development from 2000 to 2019. Two LST products extracted from Landsat 7 Enhanced Thematic Mapper (ETM+) through Google Earth Engine at 30 m resolution and Moderate Resolution Imaging Spectroradiometer (MODIS) at 1 km resolution were compared. The influence of the spatial resolution, land cover, vegetated surfaces and seasonality on the relationship between LST and in situ Tair were examined. UHI footprint (UHIFP) and the surface UHI (SUHI) models were compared to measure the local UHI impact on rural vegetation based on the time series LST data from Landsat 7. The UHI impact on surrounding land in the suburban and rural environment from 2000 to 2019 was analyzed. Results show that MODIS LST from Terra had stronger relationships with Landsat 7 LST than those from Aqua. Tair demonstrated weaker correlations with Landsat LST than with MODIS LST in sparsely vegetated and urban areas during the summer. Due to the winter’s ability to smooth heterogenous surfaces, both LST and Tair showed stronger relationships in winter than summer over every land cover, except with coarse spatial resolutions on forested surfaces. The UHI footprint of the studied low-density suburban center is about 1.4 times larger than the urban center. All vegetated land covers experienced their maximum cooling effects well before reaching the UHIFP perimeter while urban surfaces only begin to diverge from the SUHI Gaussian model outside of the UHIFP. The similar results from both methods indicate a strong urban cover influence overpowering the dominantly distributed agricultural surfaces throughout the growing season. Moisture index was shown as the dominant variable, above vegetation health, correlating with the UHI residuals within every land cover throughout the growing season. This research 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 neighborhood structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".