Remote Sensing Time Series Analysis of Waterbody Colour Change In Northern Canada
Bibliographic record
Abstract
This study used Landsat data from 1984 to 2021 processed in Google Earth Engine to analyze the spatial and temporal pattern of waterbody colour change in Northern Canada. We created biennial composite mosaics with the 25th percentile pixel reflectance value from all valid cloud and ice-free Landsat pixels over each two-year period from 1984 to 2021. Waterbodies were defined as groups of contiguous water pixels from the Global Surface Water dataset greater than one hectare in size. According to this definition, a total of 1,453,464 waterbodies were identified and used in this study. We defined five optical change indicators: surface reflectance in the blue, green, red, and near-infrared bands, as well as turbidity calculated from the red band. The pixel values for each waterbody were first summarized zonally into median values for each waterbody in each mosaic, and then temporally, into Mann Kendall statistic and Theil Sen slope values of each change indicator for each waterbody across all mosaics. The time series statistics for each waterbody were then used as inputs to a random forest classifier to assign a value of changed or unchanged to each waterbody in the study area. The model classified 22.9% of the waterbodies as changed, with an overall accuracy of 91.8% and an AUC score of 0.95. The change was clustered in coastal areas and several inland regions. Each changed waterbody was then assigned a year of change based on the year of greatest absolute interannual change in the time series of green band reflectance. The pattern across the entire study area showed early peaks of change in 1986 and 1994, and more recent smaller peaks of change in 2008, 2012, and 2020. The pattern of temporal change was highly variable by region. This study shows promising results for the use of remote sensing to monitor waterbody change across very large areas. Furthermore, the methods outlined in this paper for creating composite mosaics and classifying waterbody colour can be easily modified and applied to new regions.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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".