An integrated GEE and machine learning framework for detecting ecological stability under land use/land cover changes
Bibliographic record
Abstract
Ecological stability (ES) is recognized as a crucial factor for sustainable development at global and regional scales. However, the importance of this factor was not considered significant. Hence, the main aim of this study was to introduce a new approach that focuses on detecting ES over the Maharloo watershed in Iran. To achieve this goal, we extracted land use and land cover (LULC) data from the Google Earth Engine (GEE) platform by applying the random forest (RF) machine learning method, which obtained Kappa statistics of 0.85, 0.86, and 0.87 for the years 2002, 2013, and 2023, respectively. We identified both stable and unstable regions based on LULC changes and employed them using machine learning to forecast the ES. The most important predictors of ecological stability were elevation, soil organic carbon index, precipitation, and salinity. The results of this research revealed that certain areas within the Maharloo watershed have experienced ecological instability in recent years, with gardens showing the highest percentage (60.65%) of instability among all land-use categories. The performance and validation of our model suggest that the study results are reliable (AUC = 0.86). This study offers detailed maps of ecological stability and trends, offering valuable insights for decision makers to support landscape conservation and restoration efforts. Overall, the findings contribute to a more comprehensive understanding of the ecological dynamics of the Maharloo watershed and provide valuable insights for sustainable development and conservation efforts in other 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".