Airborne synthetic aperture radar, digital terrain models, and geographic information systems: tools for mapping and managing large landslide hazards in southwestern British Columbia
Bibliographic record
Abstract
This research demonstrates the use of airborne synthetic aperture radar (SAR) and Geographic Information Systems (GIS) for mapping and predicting large rock landslide occurrences in southwestern British Columbia. Lineaments in the Chilliwack area are mapped using geocoded 10 m resolution 1:20,000, 1:50,000, and 1:80,000 scale C-band SAR and compared to lineaments mapped with 1:50,000 scale black and white stereo airphotos. SAR lineament length and trend correlate well with airphoto lineaments and provide additional structural information about the Chilliwack area. Testing the ability of SAR to locate specific airphoto lineaments reveals that SAR tends to find the longer airphoto lineaments. SAR is useful for detecting geomorphological landslide features such as lineaments, antislope scarps, deformation zones, and especially hummocky debris lobes, cone and fans. In areas covered by cultivation, logging activity, or mature forest, SAR proves most useful for revealing landslide debris and lineaments especially where tree canopy height corresponds subtle changes in ground morphology. Depending on the viewing geometry, SAR is able to detect landslide source and deposition zones not apparent in stereo airphotos. Spatial relationships between regional faults, lineaments, plutonic contacts, bedrock geology, slope angle, and landslide hazard occurrence are explored using the IDRISI GIS and an objective multivariate statistical methodology called weights of evidence. Weights of evidence modelling determines landslide potential for regions where representative landslide occurrences are known, estimates uncertainty, ranks predictive power of input maps, and accounts for missing or incomplete data. The method is particularly well-suited for modelling multiclass maps and proximity to linear features. The top five predictor maps are: 1) Custer Gneiss; 2) within 1300 m of a fault trace; 3) within 1200 m of a lineament; 4) Cultus Formation; 5) Chilliwack Formation, and; 5) Spuzzum Pluton. The proximity to a plutonic contact and slopes less than 20° are not positive predictors of landslide occurrence. Predictor map weights are combined and map of posterior probability for predicting landslide occurrence is created. Areas of relative high landslide occurrence include the Fraser Canyon near Yale, the slopes above Chilliwack and the upper Chilliwack Valley, and adjacent to the Ross Lake Fault south of Hope. Adding this hazard zonation map with socio-economic criteria and landslide runout models within a GIS builds risk maps essential for development planning in mountainous terrain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".