Airborne lidar as a powerful new technology for operational forest inventories and sustainable forest management
Bibliographic record
Abstract
Understanding the structure and function of forest ecosystems and their interactions with other natural systems at stand, landscape and global scales is fundamental to the development of sustainable forest management practices in British Columbia. In the forests of northwestern North America, forest structure has become a key focus of research because of its significance for timber production, biodiversity, and ecosystem function. Forest structure can be more directly addressed by silvicultural prescriptions and regulatory policy than any other aspect of stand ecology and thus has become a strong management focus as well. This FII-funded research project was designed to address two main questions related to the measurement and management of forest stand structure in BC forests: (1) Can lidar remote sensing be used to improve the quality, timeliness, and cost-effectiveness of traditional forest inventories, and (2) Can this technology facilitate the collection of other more ecologically relevant stand attributes that support long-term ecological monitoring and sustainable forest management? The research reported here was undertaken within the Coastal Western Hemlock (CWH) biogeoclimatic zone on southeastern Vancouver Island; however, findings from this study could be easily adapted for use in other forested regions of BC. The inventory component of this research was conducted on private lands owned by Weyerhaeuser BC Coastal Group, while the northern part of the Sooke Lake watershed was the focus of study for research on stand structural diversity. Preliminary results indicate that lidar data are extremely rich in all kinds of biophysical information related to vertical and horizontal canopy structure, as well as the geometry of the underlying terrain surface. Our research has resulted in the development of a number of promising new techniques for extracting attributes of forest structure from lidar data. First, an individual-tree and virtual prismsampling approach was used to measure standard inventory variables (i.e., stem density, basal area, and volume) at the plot, stand, and land-holdings level. Second, lacunarity analysis has shown significant potential for quantifying the fine-scale spatial heterogeneity of forest stands, and may therefore find direct application in the design of variable retention harvesting prescriptions, detailed habitat mapping, and old-growth surveys. In theory, these quantitative estimates of spatial heterogeneity also have the potential to significantly improve the predictive strength of traditional height-based (quantile) estimators currently used in lidar studies. More detailed findings from this study will be available as a Ph.D. dissertation, Master’s thesis, and journal publications by late fall 2004.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".