Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques
Bibliographic record
Abstract
Remote sensing (RS) and GIS are important methods for land use assessment and land cover transition. In this study, land use/land cover changes in the Ago-Owu Forest Reserve, Osun State, Nigeria have been assessed. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1986, 2002 and 2017 respectively. The three scenes corresponded to path 190 and row 055 of WRS-2 (Worldwide Reference System). The processing of the imagery was preceded by the clipping of the study area from the satellite image. The boundary of the reserve was carefully digitized and used to clip the imagery to produce an image map of the forest reserve. Using the supervised image classification procedure, training sites were used to produce land use/land cover maps. The same classification scheme was used for the 1986, 2002 and 2017 images to facilitate the detection of change. The differences in the area covered by the different polygons between the three sets of images were measured in km2. The results show that during 1986 and 2017, there is a dramatic increase of build-up areas with a change of 55.65 km2 and sparse vegetation (farmland and grassland) with a change of 53.97 km2, while a dramatic decrease of dense vegetation (forest areas) with a change of 109.61 km2. The consequence of these results is that over the years, the population of people living in the forest reserve has increased and many of them are engaged in farming, leading to an increase in farmland. In addition, logging activities continued unabated in the forest reserve, as demonstrated by a sharp increase in the deforested area within the reserve. The maps produced in this study will serve as a planning tool for the Osun State Forestry Department to plan reforestation activities for the forest reserve.
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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 | 0.001 | 0.001 |
| 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 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".