A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland
Why this work is in the frame
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Bibliographic record
Abstract
Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of climate change. The availability of cloud-based geospatial platforms has allowed for the production of wetland maps at scales previously unfeasible due to technical limitations, yet the assessment of changes to wetlands at the level of the wetland class (bog, fen, swamp, and marsh) has yet to be implemented across Canada. Class-level change information is important when considering changes and impacts to wetland functions and services. To demonstrate this possibility, this study assessed 30 years of change to wetlands across the province of Newfoundland using Landsat imagery, spectral indices, and Random Forest classification within the Google Earth Engine (GEE) cloud-computing platform. Overall accuracies were high, ranging from 84.37% to 88.96%. In a comparison of different classifiers, Random Forest produced the highest over accuracy results and allowed for the estimation of variable importance, when compared Classification and Regression Tree (CART) and Minimum Distance (MD). The most important variables include the thermal infrared band (TIR), elevation, the difference vegetation index (DVI), the shortwave infrared bands (SWIR), and the normalized difference vegetation index (NDVI). Change detection analysis shows that bog, followed by swamp and fen, are the most common wetland classes across all time periods generally, and marsh wetlands are the least common wetland classes across all time periods respectively. The analysis also shows a general instability of wetland classes, though this is largely due to conversion from one wetland class to another. Future work may integrate RADAR data and consider weather patterns. The results of this study elucidate for the first time patterns of wetland class change across Newfoundland from 1985 to 2015 and demonstrate the potential of the GEE and Landsat historical imagery to assess change at provincial and national scales.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it