Monitoring influence of urbanization on urban thermal environment using multi-temporal LANDSAT imagery: application to Da Nang city
Bibliographic record
Abstract
This study illustrates urbanization in Da Nang city and its impacts on local thermal environments using remote sensing. In this study, multi-generation Landsat images from 1990, 1995, 2000, 2005, 2010 and 2014, were used to assess the city’s urban expansion and accompanied land surface temperature (LST) characteristics. Results showed that Da Nang urban expanded twice in 2014 (8,634 ha) comparing to 1990 (4,485 ha) and high temperature anomalies were closely associated with built-up and bare-soil land-covers. As consequence, areas covered by the highest LST correspondently expanded from center of Thanh Khe, Hai Chau districts to mostly all 6 present downtown districts. The mean LST difference between the urban downtown area and the suburban area tends to increase over time, from 7.3oC in 1990 to 11.9oC in 2014. In the context of moving towards “a friendly environmental Da Nang city”, this study provides useful information for understanding the local climatic and environment changes that occurred during rapid urbanization.References An, T.T, Dieu, N.T, Minh T.P., 2011: Researching the land surface temperature in Danang city from the satellite image of Landsat 7 ETM+, National GIS Conference 2011, 14-21. Castrence, M., Nong, D.H., Tran, C.C, Young, L., Fox J., 2014: Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam, Land 2014, 3, 148-166. Chavez Jr. P. S., 1988: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data,Remote Sensing of Environment, 24 (3): 459-479. Chen, X.L., Zhao, M.Z., Li, P.X., Yin, Z.Y., 2006: Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Remote Sensing of Environment, 104, 133-146. 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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 | 0.000 |
| 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".