Quantifying land surface temperature changes associated with land cover changes
Bibliographic record
Abstract
Significant transformations are being observed globally across ecosystems driven by natural pro- cesses and human activities. Using moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) product: MOD21A1 and European Space Agency Climate Change Ini- tiative (ESA CCI) land cover (LC) product, this study provides a comprehensive analysis of global land cover change (LCC) since 2000 and their impacts on LST. Using a systematic approach, the overall mean and its annual LST variations were quantified for 484 LCC transitions derived from the 22 x 22 LC classifications, revealing distinct cooling and warming patterns. Transitions that increased vegetation cover such as bare areas to grasslands, and bare areas to sparse vegetation consistently resulted in cooling effects, with temperature decreases up to −0.45◦C at a rate of −0.030◦C/year and −0.20◦C at a rate of −0.017◦C/year respectively. Conversely, warming ef- fects were linked to deforestation, urbanization, and water loss. Transitions such as flooded veg- etation to needleleaf forests (+0.84◦C) at a rate of +0.076◦C/year and cropland to urban areas (+0.39◦C) at a rate of +0.019◦C/year highlighted the critical role of land use in amplifying sur- face temperatures. Regional analysis revealed cooling trends in northern areas, such as Canada and Greenland, driven by vegetation recovery, while warming was prominent in tundra regions, where forest loss and snow cover reduction amplified surface heating. The largest global transi- tions included the conversion of bare areas to sparse vegetation, indicating ecological recovery in degraded regions, and the shift from sparse vegetation to grasslands, highlighting changes within the "Grass & Shrubs" classification, which experienced the highest levels of disturbance. Although many findings aligned with established patterns, anomalies such as unexpected cooling in ever- green needleleaf forest transitions and warming in tundra regions involving deciduous needleleaf forests underscored the complexities of LCC and their localized impacts on LST. The anomalies emphasize the need for further investigation into factors such as neighboring pixel effects, data ac- curacy, and climatic influences. By improving data accuracy and alignment, addressing resolution mismatches, and adopting regionalized analysis, future research can improve the understanding of the LCC-LST dynamics. Despite the challenges, these findings highlight the importance of sustainable land management practices, including reforestation and urban greening programs to mitigate the adverse effects of LCC on global and regional LST.
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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 | 0.001 | 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".