Phenology analysis of moist decedous forest using time series Landsat-8 remote sensing data
Bibliographic record
Abstract
Accurate and up–to-date monitoring of forests at mountainous regions at regular time interval is a challenging task. Remote sensing derived metrics such as vegetation indices are the most widely used tools for the estimation of forests phenological attributes and ecosystem monitoring. Thus, in this study, assessment of spectral traits has been carried out using satellite RS sensors derived information. In this study, the assessment was carried out from 2013 to the mid-2015 using Landsat-8 to understand NDVI phenology and modeling of phenology trends using temporal normalized phenology index (TNPI) and remote sensing derived variables such as land surface temperature (LST), elevation, aspect and slope within moist deciduous forest (MDF) of Doon valley of Western Himalayan of India. Results indicated that variations in topography and LST showed strong associations (p<0.001) with Landsat-8 derived TNPI. We observed that NDVI changed in lower elevation areas due to maximum change in LST. In conclusion, cross-validated statistics confirmed that TNPI was tested successfully for another MDF test site using Landsat-8 derived NDVI for two time steps of maximum and minimum vegetation growth period.
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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 | 0.001 | 0.001 |
| 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".