UC–Change: a classification-based time series change detection technique for improved forest disturbance mapping using multi-sensor imagery
Bibliographic record
Abstract
Unsupervised Classification to Change (UC–Change) is a versatile technique that detects forest disturbances in satellite images by analyzing changes in the spatial distribution of spectral classes over time. This approach can fully utilize the spectral resolution of individual sensors without requiring atmospheric correction or radiometric normalization. Resulting multisensor capabilities set UC–Change apart from established time-series change detection methods, such as Continuous Change Detection and Classification (CCDC), LandTrendr, Composite2Change (C2C), and Global Forest Change (GFC). With the growing number of Earth observation satellites, the ability to utilize diverse datasets is increasingly important for extracting information relevant to sustainable natural resource management. The algorithm’s effectiveness is demonstrated using a dataset containing 275 Landsat and Sentinel–2 images acquired over a forested area in British Columbia, Canada, from 1972 to 2020. The 100 km × 100 km study site has been actively harvested in recent decades and experienced many wildfires and a mountain pine beetle (MPB) outbreak. The spatio-temporal accuracy of clearcut and fire-scar detection was assessed using the Vegetation Resources Inventory (VRI) and National Burned Area Composite (NBAC) products, respectively, and compared against the C2C 1985 – 2020, CCDC 2002 – 2019, and GFC 2001 – 2022 maps available online. Overall, the UC–Change algorithm detected 85.2 % of the reference VRI 1974 – 2018 cutblock pixels at a temporal resolution of ± 1 year (90.3 % at ± 3 years). It detected 86.0 % of 1985 – 2018 VRI pixels, outperforming C2C (58.8 %). For the period 2002 – 2018, UC–Change mapped 87.1 % of the reference cutblock pixels, exceeding C2C (54.5 %), CCDC (74.0 %), and GFC (70.4 %). UC–Change, C2C, CCDC, and GFC detected 71.0 %, 54.6 %, 37.4 %, and 67.2 % of 2006 – 2018 reference forest fire pixels, respectively. UC–Change provided improved forest harvest and fire-scar detection in areas heavily affected by the MPB outbreak and forests characterized by low canopy cover. It represents a new, fundamentally different approach to time-series analysis, suitable for independent use or in concert with existing methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| 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) | 0.002 | 0.001 |
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".