The Role of Vegetation Health and Nature Based Solutions in Mitigating Climate Change in River Isiukhu Basin.
Bibliographic record
Abstract
Assessment of how well Nature based Solutions (NbS) can offset climate change is vital for mitigation and adaptation planning, but has rarely been done. Therefore, this study aimed to assess the role of NbS and vegetation health in mitigating the effects of climate change in River Isiukhu basin using Normalized Difference Vegetation Index (NDVI) and Normalized Difference Bare Soil Index (NDBSI). NDVI and NDBSI were derived from Google Earth Engine and ArcGIS Pro 3.2. Precipitation and temperature data was collected from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and TERRACLIMATE respectively. The relationship between remote sensing indices (NDBSI/NDVI) and temperature/precipitation were explored using Pearson correlation. Three major NbS projects were noted in the region. NDVI increased by 35% between 1990 to 2023. The increases were noted between 1990-2013 while declines were noted between 2013-2023. NDBSI decreased between 1990-2013 and increased between 2013-2023. In terms of climate variability, the overall precipitation increased by 22.7% (427.3 mm) between 1990 -2023. The mean temperature increased by 7.43% (1.5˚C) between 1990-2023. There was a positive relationship between precipitation and NDVI (r = 0.5549) and a negative correlation between precipitation and NDBSI (r = -0.139). Temperature was both positively correlated with NDVI (r = 0.8237) and NDBSI (r = 0.1916). Therefore, vegetation health and cover greatly controlled the climatic conditions of River Isiukhu basin. This study prioritises the adoption of NbS for climate change mitigation in River Isiukhu Basin. The study findings can be used as a reference for measuring the effectiveness of NbS in mitigating climate change in the world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".