Mapping vegetation structure and carbon dynamics across the Canadian forest-tundra ecotone using multi-scale remote sensing
Bibliographic record
Abstract
Climate change is impacting the stability and livelihoods of northern communities across Canada. Warming is accelerated at high-latitudes which has cascading effects on permafrost, hydrology, vegetation and carbon storage. The transitional area between the boreal and tundra biomes, known as the “forest-tundra ecotone”, is a large, dynamic region of climate-sensitive vegetation, including forests, tall-shrubs and tundra plant communities. Warming temperatures are expected to release reproductive limitations on trees facilitating northward advance of the boreal biome. However, there is significant variation in measurements of treeline advance across different locations, and the timing of advance relative to recent climatic changes are unclear. The primary objective of my dissertation is to use remote sensing datasets to investigate change in vegetation structure and its implications for carbon storage across the Canadian forest-tundra ecotone. To accomplish this, I used spaceborne light detection and ranging (Lidar) measurements and multi-spectral satellite imagery to model vegetation structure across 180 million hectares of northern Canada. These models were used to evaluate whether change in vegetation structure over the past 40-years is consistent with northward advance of the boreal biome. I found that forested area expanded northward by approximately 1 million hectares across Canada, but large parts of the limit were stable over time. Mean annual temperature and total precipitation also increased along the forest limit, indicating that the position of the forest-tundra boundary is out of equilibrium with recent climate warming. A central theme of this dissertation is assessing change across spatial and temporal scales. To this end, I also used high-resolution drone imagery to link fine-scale vegetation structure with measurements of soil organic carbon across northern tree and shrublines. At the regional scale, I paired historic air photos with contemporary satellite imagery to assess long-term patterns in tree and shrub expansion at multiple treeline sites. These patterns were linked back to national mapping products derived from multi-spectral time series to understand potential biases and evaluate strengths in both approaches. Ultimately, this dissertation provides new insights into climate driven impacts on northern vegetation across Canada, and demonstrates the use of novel remotely sensed data to support future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".