Remote sensing indices for monitoring land degradation in a semiarid to arid basin in Jordan
Bibliographic record
Abstract
Spectral reflectance for soils and vegetation of the Yarmouk basin were correlated with surficial soil properties and vegetation biomass and cover. The overall aim of the study was to identify bands suitable for assessing soil and vegetation as indices for land degradation and desertification. Results showed that vegetation was well separated from soils in the shortwave infrared wavelength at 1480 nm. For most sites, the differences in the bandwidths (in the range of 8.5 nm to 90 nm) did not improve the differentiation of vegetation types. For all wavelengths, stronger correlation values (maximum R2 = 0.85) were obtained for vegetation cover when compared with biomass (maximum R2 = 0.54). Soil spectral reflectance tended to increase with salinity, with maximum correlations obtained in the blue wavelengths (470±10 nm, 485±90 nm), followed by green and the NIR bands, where R2 values were around 0.60. Comparing results from radiometer measurements with results obtained from ASTER image bands showed that correlations tended to decrease with decreased spatial resolution for the investigated soil properties. For all wavelengths, spectral reflectance of degraded soils was higher than that for natural vegetation and irrigated crops with partial surface cover. Results of the study showed that the use of remote sensing indices related to vegetation cover and soil salinity would be recommended to map the extent of land degradation in the study area and similar environments. However, spectral unmixing should be applied to improve the correlations between satellite remote sensing data and surficial soil properties.
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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 | 0.000 |
| 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 | 0.000 | 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 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".