A Review of Dendrochronology and Remote Sensing Integration for Forest Growth and Disturbance Monitoring
Bibliographic record
Abstract
Purpose of Review: Understanding forest growth and its response to climate variability and disturbance is critical for monitoring carbon dynamics and managing forest ecosystems under global change. Tree-ring width (TRW) data from dendrochronology and vegetation indices (VIs) derived from remote sensing offer complementary perspectives on forest growth-one reflecting carbon accumulation in wood, the other photosynthetic activity by foliage and changes in canopy cover. Their complementary spatial and temporal scales also enable upscaling measurements through time and space. The review synthesizes 78 multidisciplinary studies that integrate these two disciplines to evaluate their combined potential in assessing forest growth. Recent Findings: The review revealed growing interest in combining dendrochronology and remote sensing, with diverse applications and methodological approaches, which we have grouped in three dominant areas of research: (1) examining relationships between TRW, VIs, and climate; (2) assessing long-term growth and productivity trends; and (3) evaluating responses to disturbance and extreme climatic events. We showcase a subset of relevant studies and highlight some key results, with many reporting strong interannual TRW-VIs positive relationships during growing season months. Research on growth trends shows more mixed outcomes, as the growth recorded by TRW and VIs is often decoupled over longer timescales. In disturbance-related studies, TRW generally reflects stronger and more prolonged growth reductions than VIs, suggesting it is more sensitive to stress-induced source and sink limitations. Despite methodological advances, challenges remain, including scale mismatches between ground and satellite data, limited use of high-resolution imagery, and underrepresentation of ecological metadata. Summary: Integrating dendrochronology and remote sensing enhances the spatial and temporal scope of forest growth monitoring. In this review, we summarize interdisciplinary studies, examining their methodological approaches, including sampling strategies, growth proxies, and statistical analyses. We then outline persistent challenges, including spatial biases in tree-ring datasets, scale mismatches between ground plots and satellite data, and the physiological distinctions underlying foliage activity and radial growth. Ultimately, we identify key opportunities for further development in this interdisciplinary field, such as expanding ecological metadata collection, adopting higher resolution satellite imagery, and improving our understanding of the complex physiological processes underlying forest growth. Supplementary Information: The online version contains supplementary material available at 10.1007/s40725-025-00260-w.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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; 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".