Assessing the Accuracy and Feasibility of Using Close-Range Photogrammetry to Measure Channelized Erosion with a Consumer-Grade Camera
Bibliographic record
Abstract
Water-induced channel is one of the main forms of soil erosion in cultivated fields. Channelized erosion is often measured by the volume of the channels. Traditionally, the measurements were conducted with rulers or measuring tapes. However, these traditional methods are generally time- and labor-consuming and can cause soil surface disturbance. Close-range photogrammetry with a Consumer-Grade Camera (CGC-CRP) provides an alternative way of measuring channel volume and can overcome limitations of traditional methods and provides much higher spatial resolution. However, quantitative information on the accuracy of this technique is rare. In this study, the accuracy of the CGC-CRP method under different settings were examined with an in-house experiment and validated with a field experiment. In the in-house experiment, a wood board surface with Artificial Channels (AC) of different shapes, orientations, and sizes were built. These ACs were surveyed using the CGC-CRP method with a series of settings of shooting angles and image overlapping rates. Selected cross-sectional areas were extracted to compare against manual measurements to assess the absolute and relative errors of the CGC-CRP method. The applicability of the CGC-CRP method with different settings was evaluated by comparing time consumption and the size of detection areas. The results indicated that in order to maintain an acceptable accuracy level, the image overlapping rate should be ≥70%, and the shooting angle should be in the range of 60° to 80°. For the channel shape, the accuracy for V-channel was ~15% higher than that for U-channel. For the U-channel, the impact of the channel orientation on the accuracy was not significant when the shooting angle was relatively high, whereas for the V-channel, the vertically oriented channel had higher accuracy than horizontal or angle channels. Last, channel size did not strongly affect accuracy when the channel was vertically orientated, and the shooting angle and image overlapping rate were set in the optimum ranges. However, when the shooting angle or image overlapping rate was low, or when the channel was angled or horizontally orientated, the accuracy was lower with larger channel size. In the field experiment, under the optimum camera setting, the error for the ten cross-sectional areas was about 1.6%. This result suggests that the CGC-CRP method is promising in volumetric assessment of rill and gully erosion. The quantitative information on the accuracy provided in this study can help researchers to select the setting of CGC-CRP methods to achieve their required accuracy level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".