Detection of small single trees in the forest–tundra ecotone using height values from airborne laser scanning
Bibliographic record
Abstract
Because of global warming, it is assumed that the arctic and alpine tree lines will advance northwards into the tundra and upwards into mountainous regions. Methods are needed to monitor these advances. Airborne laser scanning has recently been introduced for detection of small pioneer trees that form the advanced alpine tree line. The objective of this study was to analyze the capability of high-density airborne laser scanning data used for detecting such individual small trees in the transition between the mountain forest and the alpine zone, the forest–tundra ecotone. The study used field and laser data collected along a 1500 km transect stretching from northern Norway (69°3′ N) down to the southern part of the country (58°3′ N). In the field, 744 trees of mountain birch, Norway spruce, and Scots pine were geolocated with centimetre accuracy, and they were measured for height, root collar diameter, and crown diameter. Tree heights ranged between 0.02 and 7.80 m. The laser data were acquired in two separate acquisitions with mean pulse densities of 6.8 m−2 and 8.5 m−2, respectively. Laser echoes with relative height values greater than zero within the individual tree crown polygons were used as a criterion for a successful tree detection. The detection success for trees taller than 1 m was 90%; however, for trees shorter than 1 m, the corresponding value was 49%. The highest detection success was found for spruce. Generalized linear models and a generalized linear mixed model with binary responses (detected/not detected) were applied to evaluate the effects of tree height, tree crown area, tree species, geographic location along the latitude gradient, and region on successful detection. Although they were highly correlated, tree height and tree crown area turned out to be the variables showing high significance (p ≤0.001) in all of these models.
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 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 | 0.000 |
| 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 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".