An Investigation of Kagera River Basin Land Cover Dynamics for Sustainability of Lake Victoria Water Regime
Bibliographic record
Abstract
This paper is reporting on an investigation done to assess land cover dynamics in KRB(Kagera River Basin) (KRB) over a period of 21 years i.e.1990-2011 using Remote Sensing, Hydrology and GIS techniques as a basis for initiating policy interventions for sustainability of Lake Victoria water regime.The extent of land cover changes in the KRB have not been quantified with the view of assessing the extent of exploitation of the natural resources.The assessment is important firstly for identification of causative factors and secondly as a mechanism for formulating necessary policies and remedial measures to that effect.Specifically, Landsat 5, Landsat 7 and ETM+ (2000) satellite images were used.Eight Land Cover classes were identified namely, Forests, Woodland, Bushland, Cultivated Land, Swamps, Grassland, Bareland and Urban areas.Hydrographical data were used to assess changes in quantity of water flowing from the KRB into Lake Victoria and relating it with land cover changes in KRB at in the period of study. Spatial data processing, analysis and interpretation were carried out in ERDAS Imagine and ArcGIS software environments; while water quantity data and other non spatial data were processed and analyzed in Microsoft Excel application 2007. The study revealed a significant decline of vegetation cover and water quantity discharged from Kagera River to Lake Victoria over the study period. This contravenes the global efforts of ecological sustainability, which urge avoidance of depletion of natural resources for maintenance for ecological balance. These results are in early warning to the Management of Lake Victoria for its potential failure towards achieving the Millennium Development Goal on responsible consumption and protection of natural resources in proximity with Lake Victoria. The Management of Lake Victoria in collaboration with other relevant authorities should thus, take remedial measures to control further depletion of vegetation cover in KRB for sustainability of Lake Victoria and its ecosystem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".