Modelling Forest Inventory and Biophysical Variables for an Uneven-Aged Forest Using Multi-Source Remotely-Sensed Data
Bibliographic record
Abstract
Forest resource inventory (FRI) information is critical to sustainable forest management. Airborne Laser Scanning (ALS) offers a cost-effective option for modelling forest inventory, biophysical and ecological variables over large areas. Given that traditional ALS-based FRIs rely primarily on height data, the objective of this research was to examine the potential of ALS intensity data, multi-seasonal multispectral imagery, and digital aerial photogrammetry (DAP) for enhancing traditional ALS-based FRIs using a combination of non-parametric and parametric modelling techniques. For size class distribution estimation, the results of k-nearest neighbor imputation and random forest regression demonstrated that the combination of ALS height- and intensity-based metrics improved accuracy compared to models based on either type of metric alone. Using a hierarchical variable clustering technique, ALS intensity data were found to carry unique information complementary to passive near-infrared data, despite their similarity in wavelengths. Compared to ALS data alone, the addition of multi-seasonal imagery contributed to more accurate models of basal area and species mixture. In contrast, ALS height- and intensity-based metrics exhibited unparalleled utility for modelling stem density compared to optical imagery. Among the three multispectral sensors examined (i.e., Landsat-5 TM, Sentinel-2A and WorldView-2), Sentinel-2A proved to be the most cost-effective for enhancing ALS-based FRI, owing to its sufficient spatial resolution and inclusion of key spectral bands (i.e., red-edge and shortwave infrared). Compared to ALS, similar functional groups of metrics were found in DAP data, but DAP metrics lacked the capacity for characterizing canopy permeability. Due to the lack of penetrating echoes, gap fraction information was not well represented by DAP, resulting in suboptimal accuracy for LAI estimation compared to ALS. However, a comparison of functional groups between DAP and ALS identified tasks for which DAP is suitable (e.g., volume, forest successional stages, and species mix). Overall, this research demonstrates that ALS-based FRIs can be enhanced by additional sources of input, such as ALS intensity data and multispectral imagery; thereby demonstrating greater potential for more advanced FRIs for Canadian forests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".