Drought Monitoring in the Dry Zone of Myanmar using MODIS Derived NDVI and Satellite Derived CHIRPS Precipitation Data
Bibliographic record
Abstract
Drought has become an increasingly frequent phenomena around the globe causing negative impacts on ecosystems, agriculture, and socio-economic conditions. While efforts have been underway for developing effective monitoring and risk management measures, it still remains a challenge in countries like Myanmar where access to observed and near real time data is a constraint. This study therefore, tries to derive correlations between MODIS Normalized Difference Vegetation Index (NDVI) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data to see if some empirical relationships can be established. Statistical analysis showed that strong correlation (R² = 0.74 and 0.82) exist between NDVI and CHIRPS data indicating that vegetation stress conditions observed in the Dry Zone of Myanmar is due to insufficient precipitation conditions. The analysis also showed that the region had faced with three extreme conditions during the period from 1981-2015 with 2014 and 2015 being the extreme event. It further concluded that NDVI and CHIRPS could provide near real time information on vegetation stress situations of the Dry Zone of Myanmar.
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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 | 0.000 |
| 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 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".