Predicting the Vegetation Expansion in Selangor, Malaysia using the NDVI and Cellular Automata Markov Chain
Bibliographic record
Abstract
This study analyzed Landsat data to investigate the expansion of vegetation in the state of Selangor, Peninsular Malaysia, using the NDVI and Cellular Automate Markov Chain method. Two sets of Landsat data acquired in 2000 and 2015 were analyzed. The Vegetated areas in each Landsat scene were extracted from NDVI images. Then, the two image sets, 2000 and 2015, were input into Idrisi software where the Markov algorithm was used to predict the vegetation expansion for the year 2030. The results show that the vegetated areas increased by 45% between 2000 and 2015. Similarly, a 55% increase in vegetation for the next 15 years (2015 to 2030) was predicted. This study suggests the need for better planning and management to balance between vegetation and urban area expansion in Selangor.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".