Temporal connections between long-term Landsat time-series and tree-rings in an urban–rural temperate forest
Bibliographic record
Abstract
Time-series of satellite-derived vegetation proxies and tree-rings widths (TRW) are similar, providing temporal records of forest productivity change from different perspectives and processes. Previous research on this relationship has focused on temperature or moisture limited coniferous forests, using lower spatial resolution (e.g., 8000 m) satellites and normalized difference vegetation index (NDVI) to test relationships over 15–30 years. There is an opportunity to leverage recent advances in building Landsat (30 m) time-series to expand comparisons into new forest types (e.g., coniferous vs. deciduous), areas (e.g., fragmented forests) and over longer periods (e.g., nearly 50 years). However, a better understanding of factors that influence relationship strength is required. We compared tree-ring measurements, converted to a ring width index (RWI), and Landsat tasseled cap angle (TCA) derived canopy cover (CC) from 1972 to 2018 across 16 deciduous, mixed, and coniferous stands in southern Ontario, Canada. For all chronologies, overall relationship strength was assessed with correlation approaches (RWI-CC, both vs. climate), and shorter-term increase-decline trends were compared with segmented regression. There were significant forest type differences, with coniferous chronologies correlating stronger with CC than deciduous. Deciduous chronologies and CC had opposite connections with summer temperature, with climate warming increasing CC and coniferous RWI but not deciduous RWI from 1980 to 2010. More recent decline at most sites appears related to a major ice storm, but multiple factors may be coexisting. We tested the utility of tree-rings for validating nearly 50 years of Landsat-observed change in urban–rural temperate forests, identifying useful connections at coniferous sites. However, there are limitations to comparing long-term Landsat time-series (based on yearly summer data) with annual tree-ring growth.
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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 | 0.000 | 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 | 0.002 |
| 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 teacher head, 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".