Systematic losses in tree-canopy cover over three decades revealed by integrating complementary data sources
Bibliographic record
Abstract
Losses and gains in canopy cover of the world’s tree canopies affect carbon stocks, species habitats, water cycles, and human livelihoods. Consistent and multi-decadal global data on tree-canopy cover dynamics are needed for modelling climate scenarios, tracking progress towards restoration targets, and diverse other research, management and policy applications. However, most data only map binary ‘forest’/‘non forest’ distinctions that are regionally restricted or biassed by data gaps, and those mapping tree-canopy cover are limited to the 21st century, leaving longer-term dynamic in tree-canopy cover largely unknown. Here, we present an annual and global time-series of tree-canopy cover between 1992 and 2018. To develop these data, we integrated complementary products, using their respective strengths to compensate for weaknesses, and exploiting path dependencies in change processes to derive predictions into the data-sparse 1990s. Our model validation indicates we can accurately map tree-canopy cover (r2=0.95 [±0.01], RMSE=6.75% [±0.08], F1-score=0.97 [< ±0.01]) and our time-series show plausible broad-scale spatiotemporal patterns, as indicated by high correlations with national statistics (r2=0.94 [< ±0.01]). Our analysis of these data revealed systematic global losses in tree-canopy cover that, area-wise, substantially exceed concurrent losses or gains of treescape extents. Our analysis and data provide novel insights into global dynamics of tree cover, and can support modelling and reporting in the scope of the Kunming-Montréal Global Biodiversity Framework, the Paris Agreement, and other forest-related policies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".