Surface moisture and vegetation influences on lidar intensity data in an agricultural watershed
Bibliographic record
Abstract
Airborne laser scanning (ALS) provides more information about scanned surfaces than just elevation. Backscattered laser pulses are visually influenced by surficial reflectance properties of the terrain being mapped, such as the presence of water. In this study the impact of soil moisture and vegetation cover on laser pulse intensity was explored. The study area, an agricultural watershed in the Annapolis Valley of Nova Scotia, was scanned several times over an 18 month period using comparable survey settings. The intensity data for all acquisitions were normalized to account for range bias effects and scaled to an 8-bit range. Tests included comparing raw intensity data with range-normalized intensity data, comparing daily data to assess temporal changes in intensity, and correlating intensity data to spatially coincident soil surface volumetric moisture content measurements. The range-normalized intensity comparison revealed that while normalization removed the majority of systematic bias in the intensity some artefacts remained in overlapping scan areas. Temporal intensity variations were observed in agricultural fields, and while some of this change was attributable to changes in the surface wetness, crop cover and the confounding influence this had on laser pulse attenuation diminished the correlation. Ground sampled volumetric moisture content and intensity were not strongly correlated; however, it was shown that the two methods were measuring similar trends over some of the areas studied. This study concludes that while soil moisture conditions can influence laser return intensity over bare earth, vegetated ground cover has a greater overall control on the signal intensity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".