Assessing Pine Forest Restoration Post-Fire Using NDVI and GIS Technologies
Bibliographic record
Abstract
This study addresses the pressing and pertinent issue of establishing a space-based monitoring system to track forest condition changes.Utilizing geographic information system technologies and the Normalized Difference Vegetation Index (NDVI), it examines the evolution and dynamics of pine forest regeneration in the Semey Ormany State Forest Natural Reserve (Zhanasemey branch), Kazakhstan.Landsat-5 satellite data were employed to map and identify terrestrial vegetation changes within the Zhanasemey branch.Satellite imagery for June 2007 and September 2008 were specifically utilized for this investigation.Post-acquisition, the satellite images were subjected to corrections, followed by radiometric adjustments to minimize atmospheric interference.Subsequently, NDVI classifications, an established method for plant classification, were applied to each image.The study focused on a 15-year timespan (2008 to 2022) to monitor changes in vegetation intensity, classified according to NDVI, and to track the reforestation process in areas affected by fire.Analysis of the regeneration of the pine forest post the 2008 fire, conducted at five-year intervals, revealed an increase in the restored forest area from 6,773.49ha in 2013 to 10,721.07 ha in 2018, and finally to 14,742.54ha in 2022.In 2008, the fire-impacted area with reduced vegetation spanned 7,015.41ha, while by 2022, the restored area encompassed 14,742.54ha, indicating a significant enhancement in the pine forest restoration process.The findings underscore the critical role of remote sensing in forest fire modelling and identifying areas at risk, suggesting its potential utility in large-scale monitoring and management efforts.The study demonstrates the feasibility and efficiency of using space-based technologies to monitor long-term changes in forest conditions and to track reforestation dynamics in fire-affected regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".