Assessment of Vegetation Cover Degradation Using Remote Sensing and GIS Techniques along Sudanese Red Sea Coast (Suakin to Ashad)
Bibliographic record
Abstract
This study aimed to assess the vegetation cover degradation in the Sudanese Red Sea coast (from Suakin to Ashad) after the drought during the period from 2000 - 2011. Remote Sensing and GIS techniques were used beside field survey to conduct the study. Moderate Resolution Imaging Spectrometer (MODIS) terra 2000 -2001, 2005-2006 and 2010-2011 time-Series images mainly the 16 days Normalized Difference Vegetation Index (NDVI) product and Enhanced Thematic Mapper plus (ETM+) images dated 2005 and 2010 were used. Unsupervised classification methods were used to detect vegetation cover of the study area. Based on field survey investigations, beside the data collected on the study area and image interpretation, it was evident that season 2005-2006 and season 2006-2010 are good seasons in the vegetation cover compared to season 2000-2001. Five land cover classes were detected; wet land, bare land and three classes of vegetation cover (dense vegetation, moderately dense vegetation and sparse vegetation cover). Spectral signatures of the three dominant land cover vegetation species were detected. Areas of the three classes of vegetation cover area (dense vegetation, moderately dense vegetation and sparse vegetation cover) were calculated per km2. The study concluded that MODIS could be used as a cost effective tool in assessing land cover changes and monitoring vegetation cover degradation.As well, it could also be used to detect fairly the different vegetation species in arid and semiarid 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".