Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale
Bibliographic record
Abstract
The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".