Analysing Changes in Land Use and Land Cover (LULC) For C81 Catchment of the Free State, South Africa
Bibliographic record
Abstract
The purpose of the study is to analyse changes in land use and land cover (LULC) from 1990-2018 in the C81 catchment of the Free State, South Africa. It is important to understand the changes of LULC on the catchment and evaluate its impact on environmental aspects. Changes in LULC were examined using remote sensing, and aeronautical reconnaissance coverage geographic information system (ArcGIS). The LULC data was obtained from the South African National Land Cover (SANLC) project. The results of the net change from 1990-2018 with 35 classes of LULC show that the most reduced are forest plantations which decreased from 57.65% to 1.02%, and low shrublands which decreased from 17.08% to 0.16%. However, the most expanded are grasslands which increased from 17.74% to 54.30% and cultivated agriculture which increased from 1.11% to 37.32%. The transition shows that the majority of the LULC changes occurred in grassland with an annual rate of change of 1,31% and forest plantation with an annual rate of change of -2,02%. These changes in LULC may be attributed to population increase, the severity of drought and floods, water scarcity for irrigation, and climate change effects. Grasslands to forest plantations were recorded with the highest changes, which indicated that the area is dominated by agricultural activities. However, plantations or woodlots to wetlands recorded the lowest changes, which indicates low rainfall in the study area for the period under review. Integrated LULC management of this catchment is, therefore, a necessity for the mitigation of environmental degradation.
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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 | 0.000 | 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".