COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE ON LANDSAT 8 AND HLS-L30 USING MONO WINDOW AND SPLIT WINDOW ALGORITHMS (CASE STUDY: WKP MOUNT UNGARAN)
Bibliographic record
Abstract
Advancements in remote sensing technology have enabled the use of satellite imagery, such as Landsat 8 and HLS-L30, for the spatial and temporal estimation of Land Surface Temperature (LST) with improved resolution. In the context of geothermal exploration, the availability of thermal infrared bands in these datasets facilitates more efficient and cost-effective mapping and identification of surface temperature anomalies, particularly across large and inaccessible areas. This study aims to compare LST estimations derived from Landsat 8 and HLS-L30 imagery using the Mono Window Algorithm (MWA) and Split Window Algorithm (SWA) at 18 geothermal manifestation points within the Mount Ungaran Geothermal Working Area (WKP). A Focal Statistic process was applied to 20 LST datasets, resulting in a total of 100 LST layers. From each layer, LST values were extracted at the 18 manifestation points, producing a total of 1,800 data points. A binary logistic regression analysis was conducted using these LST values alongside those from 20 randomly selected comparison points. The results indicate that the median LST derived from HLS-L30 imagery using the Split Window Algorithm with the minimum Focal Statistic yielded the most optimal performance in classifying geothermal manifestation presence. This method achieved statistical significance (p = 0.028), indicating its capability to effectively distinguish between manifestation and non-manifestation points. However, the pseudo-R² value of 0.107 suggests that the model explains approximately 11% of the variance in the data. These findings underscore the potential application of satellite-based LST analysis in the early detection and assessment of geothermal surface anomalies within WKPs.Keywords : Geothermal, LST, Landsat, HLS-L30, Ungaran
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".