Chromaticity analysis of LANDSAT Multispectral Scanner and Thematic Mapper imagery of Chilko Lake, British Columbia, using a theoretical optical water quality model
Bibliographic record
Abstract
Chromaticity analysis of LANDSAT Multispectral Scanner (MSS) imagery of Chilko Lake, B.C. reveals a. locus whose shape has not been previously reported. To investigate the cause of this and to come to a broader understanding of chromaticity analysis for MSS and Thematic Mapper (TM) data, an optical water quality model has been used. The model is composed of a four component reflectance model (R-model), an interface model and an atmospheric model. The R-model was calibrated for Chilko Lake by determining the specific absorption and backscattering spectra for suspended minerals (SM), chlorophyll-a uncorrected for phaeophytins (C) and yellow substance (YS). The fourth component is water. The model reproduces the observed locus shape and indicates that it is primarily a function of SM, with the unreported lower limb on MSS imagery caused by SM gradients with concentrations less than 1-2 mg/L. The effects of C, YS and SM cannot be separated on plots of chromaticity coordinates X and Y for either MSS or TM data. In addition, haze or wind gradients, if they occur over water with low levels of SM, would look similar to the lower limb on MSS XY plots. However, if brightness is used in combination with X, the model predicts that C and YS, though themselves inseparable, can be differentiated from SM at all but the lowest concentrations of SM. Furthermore, haze and wind gradients can be distinguished from the lower limb. Thus the addition of brightness to chromaticity analysis has the potential to significantly improve the technique. The model was tested by comparing simulated chromaticity results with results from actual images (one TM image and three MSS images) for which ground truth had been collected. Qualitative predictions regarding haze and water quality patchiness were confirmed. Correlation analysis with R² values from 0.81 to 0.95 also strongly confirmed predictions regarding SM, but showed that the model is systematically underestimating SM. Correlation tests for a combined C and YS factor (CYS) were inconclusive because of the systematic modeling error, but classification maps provide weak evidence that CYS is behaving qualitatively as predicted and that CYS can be differentiated from SM. The modeling error is thought to originate in atmospheric assumptions which are not met. The R-model which is fundamental to the study has been tested and is not a major source of error. The study concludes that the model is qualitatively correct and that the use of brightness improves chromaticity analysis by allowing separation of CYS and SM, though further work should be undertaken to verify these results. Maps of CYS and SM in Chilko Lake reveal that CYS tends to be higher along the western shore and where the hypolimnion is exposed. SM are highest near stream mouths. The distribution patterns are related to physical processes within the lake and provide a synoptic view of the connection between water quality parameters and circulation which would be difficult to achieve in any other way.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".