Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series
Bibliographic record
Abstract
This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests. The rate of post-disturbance vegetation recovery is an important component of forest dynamics. In this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied. We used LTS surface reflectance data (1999–2015).We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data. The coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems. The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".