Spatial patterns of logging-related disturbance events: a multi-scale analysis on forest management units located in the Brazilian Amazon
Bibliographic record
Abstract
Abstract Context Selective logging has been commonly mapped using binary maps, representing logged and unlogged forests. However, binary maps may fall short regarding the optimum representation of this type of disturbance, as tree harvest in tropical forests can be highly heterogeneous. Objectives The objective of this study is to map forest disturbance intensities in areas of selective logging located in the Brazilian Amazon. Methods Selective logging activities were mapped in ten forest management units using Sentinel-2 data at 10 m resolution. A spatial pattern analysis was applied to the logging map, using a moving window approach with different window sizes. Two landscape metrics were used to derive a forest disturbance intensity map. This map was then compared with actual disturbances using field data and a post-harvest forest recovery analysis. Results Disturbed areas were grouped into five distinct disturbance intensity classes, from very low to very high. Classes high and very high were found to be related to log landings and large felling gaps, while very low intensities were mainly related to isolated disturbance types. The post-harvest forest recovery analysis showed that the five classes can be clearly distinguished from one another, with the clearest differences in the year of logging and one year after it. Conclusions The approach described represents an important step towards a better mapping of selectively logged areas, when compared to the use of binary maps. The disturbance intensity classes could be used as indicators for forest monitoring as well as for further evaluation of areas under forest 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".