China's annual forest age dataset at a 30 m spatial resolution from 1986 to 2022
Bibliographic record
Abstract
Forest age is crucial for both carbon cycle modeling and effective forest management. Remote sensing provides crucial data for large-scale forest age mapping, but existing products often suffer from a low spatial resolution (typically 1000 m), making them unsuitable for most forest stands in China, which are generally smaller than this threshold. Recent studies have generated static forest age products for 2019 (CAFA V1.0) (Shang et al., 2023a) and 2020 (Cheng et al., 2024) at a 30 m spatial resolution. However, their low temporal resolution limits their applicability to track multiyear forest carbon changes. This study aims to generate China's annual forest age dataset (CAFA V2.0) at a 30 m resolution from 1986 to 2022, utilizing forest disturbance monitoring and machine learning techniques. Forest disturbance monitoring, which typically has lower uncertainty compared to machine learning approaches, is primarily employed to update annual forest age. The modified COLD (mCOLD) algorithm, which incorporates spatial information and bidirectional monitoring, was used for forest disturbance monitoring. For undisturbed forests, forest age was estimated using machine learning models trained separately for different regions and forest cover types, with inputs including forest height, vegetation indices, climate, terrain, and soil data. Additionally, adjustments were made for underestimations in the Northeastern and Southwestern regions of China identified in CAFA V1.0 using additional reference age samples and region-specific and forest-type-specific models. Validation, using a randomly selected 30 % of two reference datasets, indicated that the mapped age of disturbed forest exhibited a small error of ±2.48 years, while the mapped age of undisturbed forest from 1986 to 2022 had a larger error of ±7.91 years. The generated 30 m annual forest age dataset can facilitate forest carbon cycle modeling in China, offering valuable insights for national forest management practices. The CAFA V2.0 dataset is publicly available at https://doi.org/10.6084/m9.figshare.24464170 (Shang et al., 2023b).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".