A linear regression method for tree canopy height estimation using airborne lidar data
Bibliographic record
Abstract
Tree canopy height is one of the most fundamental measurements in forest inventory and is a critical variable in the quantitative assessment of tree (or stand) volume, forest biomass, carbon stocks, growth, and site productivity. In this study, we analyzed two traditional methods for tree canopy height estimation and designed a new linear regression method for improved tree canopy height estimation using airborne light detection and ranging (lidar) data. Examples of two typical crown shapes were used, and theoretical analysis was performed on simulated datasets with varying crown shape, unit penetrability, and laser-missed canopy layer(s). The final result derived from the simulated lidar data illustrates that the linear regression method can improve canopy height estimation. This method was also applied to lidar data covering a tall pine forest in Idaho, USA. An average error of 0.51 m was obtained from a comparison of the lidar-derived tree canopy heights and 79 field measurements. This error was also compared with the estimation error resulting from the use of two traditional methods. Results indicate our method produced more accurate tree canopy height estimates, with a mean error and root mean square error (RMSE) ranging between 25% and 50% lower than those from the two traditional methods.
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 | 0.001 | 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 | 0.001 | 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 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".