Burned Area Detection in the Eastern Canadian Boreal Forest Using a Multi-Layer Perceptron and MODIS-Derived Features
Bibliographic record
Abstract
Wildfires play a critical role in boreal forest ecosystems, yet their increasing frequency poses significant challenges for carbon emissions, ecosystem stability, and fire management. Accurate burned area detection is essential for assessing post-fire landscape recovery and fire-induced carbon fluxes. This study develops, compares, and optimizes machine learning (ML)-based models for burned area classification in the eastern Canadian boreal forest from 2000 to 2023 using MODIS-derived features extracted from Google Earth Engine (GEE), and the feature extraction includes maximum, minimum, mean, and median values per feature to enhance spectral representation and reduce noise. The dataset was randomly split into training (70%), validation (15%), and testing (15%) sets for model development and assessment. Combined labels were used due to class imbalance, and the model performance was assessed using kappa and the F1-score. Among the ML techniques tested, deep learning (DL) with a Multi-Layer Perceptron (MLP) outperformed Support Vector Machines (SVMs) and Random Forest (RF) by demonstrating superior classification accuracy in detecting burned area. It achieved an F1-score of 0.89 for burned pixels, confirming its potential for improving the long-term wildfire monitoring and management in boreal forests. Despite the computational demands of processing large-scale remote sensing data at 250 m resolution, the MLP modeling approach that we used provides an efficient, effective, and scalable solution for long-term burned area detection. These findings underscore the importance of tuning both network architecture and regularization parameters to improve the classification of burned pixels, enhancing the model robustness and generalizability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".