Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar
Bibliographic record
Abstract
In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include “logging”, “plantation”, “shifting cultivation”, “urban expansion”, “water invasion”, “recovery”, “other change”, and “no change”. The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".