Deforestation detection and monitoring in cedar forests of the moroccan Middle-Atlas mountains
Bibliographic record
Abstract
The main objective of this study was to identify areas of deforestation/reforestation in the Middle-Atlas cedar forest and monitor their temporal dynamics. The aim was to detect a detailed "from-to" change information; it targets a quantitative estimation of the extent and the magnitude of the changes affecting major identified species of the Moroccan cedar ecosystem: cedar, oak, and deciduous. The major challenge was to identify changes of interest such as identifying the change due to a selective logging which consists on cutting cedar canopy trees while sparing the understory oak trees. To address these issues and achieve our objectives, we adopted a methodology with two main stages. First, we mapped major forest species from multidate satellite images (Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) using maximum likelihood classification (MLQ and spectral mixture analysis (SMA). Second, we performed change detection assessment using two procedures: (i) image products differencing to assess the overall change in the forest cover, (ii) post-classification comparisons using the outputs of the MLC and the relative abundances of forest species as determined by SMA. Results have shown the following: logging has decreased cedar area in a proportion of 12% while reforestation has yielded an increase of 8% in cedar forest; in oak forest, the increment (21%) has exceeded the deforestation effect (17%); conversely, deciduous have either degraded (11%) or remained stable (21%).
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 | 0.000 |
| 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".