Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine
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Bibliographic record
Abstract
Carbon stocks in forest ecosystems, when released as a result of forest degradation, contribute to greenhouse gas (GHG) emissions. To quantify and assess the rates of these changes, the Intergovernmental Panel on Climate Change (IPCC) recommends that the REDD+ mechanism use a combination of Earth observational data and field inventories. To this end, our study characterized land-cover changes and forest-cover dynamics in Togo between 1985 and 2020, using the supervised classification of Landsat 5, 7, and 8 images on the Google Earth Engine platform with the Random Forest (RF) algorithm. Overall image classification accuracies for all target years ranged from 0.91 to 0.98, with Kappa coefficients ranging between 0.86 and 0.96. Analysis indicated that all land cover classes, which were identified at the beginning of the study period, have undergone changes at several levels, with a reduction in forest area from 49.9% of the national territory in 1985, to 23.8% in 2020. These losses of forest cover have mainly been to agriculture, savannahs, and urbanization. The annual change in forest cover was estimated at −2.11% per year, with annual deforestation at 422.15 km2 per year, which corresponds to a contraction in forest cover of 0.74% per year over the 35-year period being considered. Ecological Zone IV (mountainous, with dense semi-deciduous forests) is the one region (of five) that has best conserved its forest area over this period. This study contributes to the mission of forestry and territorial administration in Togo by providing methods and historical data regarding land cover that would help to control the factors involved in forest area reductions, reinforcing the system of measurement, notification, and verification within the REDD+ framework, and ensuring better, long-lasting management of forest ecosystems.
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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 it