Harmonizing multi-temporal airborne laser scanning point clouds to derive periodic annual height increments in temperate mixedwood forests
Bibliographic record
Abstract
When combining multi-temporal airborne laser scanning (ALS) data sets, forest height growth assessments can be compromised due to variations in ALS acquisitions. Herein, we demonstrate the importance of assessing and harmonizing the vertical alignment of multi-temporal ALS data sets used for height growth calculations. Using four ALS acquisitions (2005–2018) in a temperate mixedwood forest, we developed an ALS data harmonization approach and quantified the impact of the harmonization on derived height periodic annual increment (PAI), comparing the ALS-derived PAI to PAI derived from non-harmonized ALS data sets and field measurements. We found significant differences in PAI derived from harmonized and non-harmonized data, and these differences were greater for shorter growth intervals. Data harmonization resulted in a consistent PAI series that reduced uncertainties associated with the different ALS acquisitions. Although overall there was a strong relationship between field and ALS height measures ( R 2 ≥ 0.88), we found a weak relationship between the field- and ALS-derived PAI ( R 2 = 0.12). We identified systematic errors in field-based tree height measures in plots with complex crowns, tall trees, and restricted visibility. We demonstrate the need for harmonizing multi-temporal ALS data sets for the generation of PAI and, likewise, highlight the need of carefully scrutinize field-measured heights and associated increments.
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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 | 0.002 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".