Investigating boreal forest successional stages in Alaska and Northwest Canada using UAV‐LiDAR and RGB and a community detection network
Bibliographic record
Abstract
Abstract Boreal forests are a key component of the global carbon cycle, forming North America's most extensive biome. Different successional stages in boreal forests have varying levels of ecological values and biodiversity, which in turn affect their functions. A knowledge gap remains concerning the present successional stages, their geographic patterns and possible successions. This study develops a novel application of UAV‐LiDAR and Red Green Blue (RGB) data and network analysis to enhance our understanding of boreal forest succession. Between 2022 and 2024, we collected UAV‐LiDAR and RGB data from 48 forested sites in Alaska and Northwest Canada to (i) identify present successional stages and (ii) deepen our understanding of successional trajectories. We first applied UAV‐derived spectral and structural tree attributes to classify individual trees into plant functional types representative of boreal forest succession, amely, evergreen and deciduous . Second, we built a forest‐patch network to characterize successional stages and their interactions and assessed future stage transitions. Finally, we applied a simplified forward model to predict future dynamics and highlight different successional trajectories. Our results indicate that tree height and spectral variables are the most influential predictors of plant functional type in random forest algorithms, and high overall accuracies were attained. The network‐based community detection algorithm reveals five interconnected successional stages that could be interpreted as ranging from early to late successional and a disturbed stage. We find that disturbed sites are mainly located in Interior and Southcentral Alaska, while late successional sites are predominant in the southern Canadian sites. Transitional stages are mainly located near the tundra‐taiga boundary. These findings highlight the critical role of disturbances, such as fire or insect outbreaks, in shaping forest succession in Alaska and Northwest Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".