Spatiotemporal Dynamic of Land Use/Land Cover Changes and Their Drivers in the Fincha' a-Neshe Sub-Basin, Southeastern Blue Nile Basin, Ethiopia
Bibliographic record
Abstract
It is paramount to evaluate the spatiotemporal dynamics of land use and land cover (LULC) changes and their drivers. This is because it helps generate information on biodiversity, land productivity, ecology, and livelihoods for decision-making. Due to land degradation, deforestation, shifting cultivation, high population pressure, and the three national megaprojects (NMPs), the Fincha'a Neshe sub-basin (FNSB) LULC, changes may be unprecedented. This study aimed to investigate the spatiotemporal dynamics of LULC changes and their drivers using remote sensing (RS) data and geographic information systems (GIS). Landsat images 5, 7, and 8 were used for the discrete periods of 1986, 2000, and 2016, respectively. Field observations (Ground control points) and interviews were conducted with key participants to validate the data. Supervised classification with a maximum likelihood algorithm was used to classify the Landsat imagery. The results showed that the FNSB experienced substantial changes in LULC between 1986 and 2016, of which 13.8% (457.3 km2 ) were due to NMPs. The cropland cover has expanded by 694.4 km2 (57.81%) at a rate of 24.60 km2 year-1 at the expense of shrubland, forest, wetland, and grassland. In contrast, shrubland, forest, wetland, and grassland have declined at the rates of 16.8, 3.9, 3.4, and 1.7 km2 year-1 over the entire study period. Population growth and NMPs were the principal drivers of the changes in the LULC of the sub-basin. Thus, the LULC transformation rate observed in the sub-basin requires due attention and mitigation strategies, as it might seriously threaten the sustainability of natural resources and NMPs.
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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".