Remote sensing of natural disturbance caused by insect defoliation and dieback: a review
Bibliographic record
Abstract
Preface The objective of this report is to review the requirements for disturbance information, the manifestation of damage patterns that may be encountered, and to provide an overview of remote sensing sensors and change detection methods that have been, or could be applied to mapping of insect defoliation and aspen dieback. It was developed with financial support from the Canadian Space Agency (CSA) by the Canadian Forest Service (CFS) and the Canada Centre for Remote Sensing (CCRS) as part of a Government-Related Initiatives Program (GRIP) project entitled "Gauging the Health of Canada's Forests: Accounting for Insect Defoliation and Dieback in the Indicators of Sustainability for Canadians". The report was first submitted as a deliverable to the CSA in 2007 and reviews the utility of both optical and radar remote sensing sensors and change detection approaches. For this release of the report as a Geomatics Canada Open File, the text was revised in part to relay major developments regarding the availability of satellite sensors and change detection methods in particular. A comprehensive review of literature published after 2007 was beyond the scope of the revision. A recent review paper by Hall et al. (2016) draws from this report but is limited to a discussion of the utility of optical sensor systems and change detection 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".