Assessment of global land cover changes using satellite data: intermittent and long-term land cover changes from 2001 to 2020
Bibliographic record
Abstract
Abstract Global land cover has changed during the past decades, influencing biogeochemical cycles and the global climate system. This study aimed to improve understanding of global land cover dynamics to enable more effective future land management practices and conservation actions. This study quantified interannual changes in global land cover types from 2001 to 2020 and distinguished intermittent transitions from stable gains and losses. From the interannual perspective, we found that global barren lands, forests, shrublands, and snow-covered areas decreased by 5281, 1804, 952, and 188 kha yr−1, respectively. In contrast, grasslands, croplands, urban areas, and water bodies increased at 6529, 1407, 237, and 51 kha yr−1, respectively, from 2001 to 2020. According to the definitions provided in this paper, of the global forest areas, 75% was Stable (no change), 4% was Gain, 5% was Loss, and 16% was Unstable. Of the cropland areas, 56% was Stable, 9% was Gain, 9% was Loss, and 26% was Unstable. Hotspots for forest loss were Brazil, the Rest of South America, and Sub-Saharan Africa, and grassland was the most common land cover classification following forest loss. The global cropland expansion hotspots were Brazil, Canada, China, India, and the Rest of South America. The cropland gains were mainly converted from grasslands. On the other hand, barren areas in China and Middle Eastern and North Africa were changed to grasslands. A certain amount of shrublands were changed to forest in temperate regions. This paper provided land cover changes at a 500 m spatial resolution as a benchmark for future assessments. The findings showed that unstable pixels play an important role in determining the sources of uncertainty when assessing land cover changes using satellite data. Land cover assessments are sensitive to the time steps used for analysis and the definition of changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".