Integration of LIDAR, optical remotely sensed, and ancillary data for forest monitoring and Grizzly bear habitat characterization / Integração de LIDAR, sensores remotos óticos e dados auxiliares para o monitoramento fl orestal e caracterização do habitat dos ursos Grizzly
Bibliographic record
Abstract
Forest management and reporting information needs are becomingincreasingly complex in Canada. Inclusion of timber and non-timber considerations for both management and reporting has resulted inopportunities for integration of data from differing sources to provide the desired information. Canada’s forested land-base is over 400million hectares in size and fulfi lls important ecological and economic functions. In this communication we describe how remotely senseddata and other available spatial data layers capture different forestcharacteristics and conditions, and how these varying data sources may be combined to provide otherwise unavailable information. For instance, light detection and ranging (LIDAR) confers information regardingvertical forest structure; high spatial resolution imagery captures (indetail) the horizontal distribution and arrangement of vegetation andvegetation conditions; and, moderate spatial resolution imagery providesconsistent wide-area depictions of forest conditions. Furthermore, coarsespatial resolution imagery, with a high temporal density, can be blended with data of a higher spatial resolution to generate moderate spatialresolution data with a high temporal density. These remotely sensed datasources, when combined with existing spatial data layers such as forest inventory and digital terrain models, provide useful information thatmay be used to address, through modelling, questions regarding forest condition, structure, and change. In this communication, we discuss the importance of data integration and ultimately, information generation, inthe context of Grizzly bear habitat characterization. Grizzly bear habitat in western Canada is currently undergoing pressure from a combination of anthropogenic activities and a widespread outbreak of mountain pine beetle, resulting in a variety of information needs, including: detailed depictions of horizontal and vertical vegetation structure over large areasto support bark beetle susceptibility mapping and habitat modelling;moderate spatial resolution data to capture changes in infestation conditions over time to support change detection and wall-to-wallmapping; and, coarse spatial resolution data to provide increased temporaldetail enabling capture of within-year alterations to Grizzly habitat.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".