Exploring Bi-Temporal Hyperspectral Imagery For Post-Fire Landcover And Burn Severity Mapping Using Data-Driven Methods
Bibliographic record
Abstract
The increasing frequency and intensity of global forest fires necessitate advanced methods for accurate post-fire burn severity and landcover mapping. This study investigates the 2014 King Fire in California’s Sierra Nevada Mountains to explore the potential of bi-temporal hyperspectral imagery and data-driven approaches for fine-grained post-fire mapping. Utilizing pre- and post-fire Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, the study compares traditional machine learning methods with advanced deep learning techniques. Results demonstrate the superiority of advanced patch-based deep learning models (e.g., SSTN, FPGA, and SpectralFormer), which leverage spatial and spectral relationships to achieve higher classification accuracy than pixel-based methods (e.g., random forest, support vector machine, XGBoost, and 1D-CNN). The feature importance analysis highlights the unique advantages of hyperspectral data, particularly in spectral bands beyond the range of multispectral satellites such as Landsat-8 and Sentinel-2. Furthermore, the simultaneous mapping of burn severity and landcover is significantly enhanced by integrating bi-temporal data. This research underscores the transformative potential of multi-temporal hyperspectral imagery and advanced algorithms in wildfire impact analysis and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".