Wetland Use/Cover Changes and Local Perceptions in Uganda
Bibliographic record
Abstract
With increasing population, coupled with land shortage and weather variations, wetlands in Uganda have continued to face degradation due to mainly conversion for agricultural, industrial and settlement purposes. The objective of this study was to determine the spatial and temporal wetland use/cover changes and local perceptions attributed to these changes. The study utilized three sets of ortho-rectified and cloud free Landsat TM/ETM+/MSS temporal images (30 m) of 1986, 2000 and 2011. The classification procedures were carried out using an Integrated Land and Water Information System (ILWIS) software version 3.7. A wetland classification system for Uganda developed by the National Biomass Study, 2003 was adopted to describe the wetland use/cover types. The classified images were validated in a ground truthing exercise using Global Positioning System (GPS) to improve on the classification accuracy. Key informant interviews and focus group discussions were conducted with communities adjacent to the wetlands in each of three of the ten Ugandan agro-ecological zones to determine the underlying drivers of wetland use/cover changes, while household interviews generated information on local perceptions of the changes. Significant changes were mainly observed in wetland use/cover between 1986 and 2011. Major factors responsible for these changes were subsistence farming due to intensification of growing paddy rice in Kyoga plains, an influx of migrants who accessed wetlands for daily subsistence (livestock grazing) in South western farmlands and proximity to urban centres in the Lake Victoria Crescent. In all the sampled agro-ecological zones, increased crop farming in wetlands was due to changing opportunities created by existent large markets for wetland crops. Majority (60%) of the local people perceived wetlands in their proximity to have undergone high degradation within the last 10 years, and to have declined in quantity and quality of vegetation, soil fertility and water levels. There was a noticeable variation across the sampled agro-ecological zones, with the highest proportion of local communities perceiving degradation being in Kyoga plains (76%), followed by Lake Victoria crescent (63%) and South-western farmlands (41%). Locally perceived threats to wetlands were mainly from crop growing that accounted for 33% of the frequency of mentioned threats, collection of wetland resources (30%), and prolonged floods and droughts (12%). This study confirms the importance of economic opportunities from new market outlets and migration in its various forms as key factors in land use change, especially at timescales of a couple of decades.
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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 | 0.002 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".