InSAR coherence and phase information for mapping environmental indicators of opencast coal mining: a case study in Jharia Coalfield, Jharkhand, India
Bibliographic record
Abstract
Mapping and monitoring the environmental indicators of opencast coal mining can help immensely for efficient planning and management of mining operations, assessment of environmental impacts on land use, and execution of reclamation measures. Previous workers have found that areas of opencast mining can be delineated efficiently from optical multispectral data. However, it is difficult to distinguish unreclaimed abandoned or closed opencast mines from the active opencast mines in optical multispectral images. Similarly, overburden dumps cannot be distinguished from flat-lying degraded lands and recently reclaimed quarries covered with overburden materials. Overburden materials have a spectral response similar to that of bare rock outcrops and therefore are difficult to separate. In addition, coal dumps have a spectral response similar to that of the opencast mining area, occupied by coal seams and coaliferous matter, and therefore are also difficult to separate. In this work, the differential temporal decorrelation criterion of the terrain elements in InSAR data pairs has been utilized to distinguish unreclaimed abandoned or closed opencast mines from active opencast mines. Coherence information of InSAR data pairs with diverse temporal baselines provides temporal decorrelation of the scattering elements in individual resolution cells or pixels of the terrain elements. Contrast in coherence values between abandoned or closed opencast mines and active opencast mines or other land use - land cover classes is more pronounced in the InSAR data pairs with shorter temporal baselines. Shorter temporal baseline InSAR data pairs also facilitate the generation of a high-quality DEM from InSAR phase information due to substantially less temporal decorrelation noise. An ERS SAR tandem data pair with a 1-day temporal baseline was found to be the best suited for delineating unreclaimed abandoned or closed opencast mines from the active opencast mines among all the data pairs. High overall coherence in the data pair also facilitates the generation of a good quality, spatially consistent DEM from which overburden and coal dumps could be successfully delineated from the surrounding areas due to their higher relative elevations.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".