Détection des changements au niveau d’un couvert forestier en milieu semi-aride entre 1984–2009: Cas de la forêt de Senalba Chergui de Djelfa (Algérie)
Bibliographic record
Abstract
The main objective of this work was to detect changes in a semi-arid forest using Landsat satellite multi-temporal imagery and to circumvent the lack of data on landscape temporal changes. The differential-algebraic method (subtraction) was used on 12 remote sensing variables obtained at different fixed periods. Before applying the differentiations on remote sensing data, a geometric correction and a radiometric normalization of satellite images were carried out to allow images comparison. Differentiation of individual bands (TM4 and TM5), ratios (TM4/TM5 and TM7/TM5), indexes (NDVI and NDMI), Tasseled Cap differentiation (e.g. TCB [Brightness], TCG [Greenness] and TCW [Wetness]) and finally the differentiation of the first three ACPs (ACP1, APC2 and ACP3) were carried out in order to select the best data for the detection of changes. Many radiometric thresholds were tested (11 values) to identify which ones had the ability to determine the best changes in the different studied periods. The most significant values of global precision (GP) (greater than 72% for the four studied periods) obtained in the error matrices were those of the NDVI variable using 0.9δ (δ = standard deviation) at the mean. This allowed the calculation of NDVI changes and the positive, negative and stable proportions for each period. The use of recent very high resolution spatial images for validation allowed us to highlight the causes of changes and the impact of silvicultural works during the period of forest management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".