Photogrammetric monitoring of small streams under a riparian forest canopy
Bibliographic record
Abstract
Abstract The recent advent of digital photogrammetry has enabled the modeling and monitoring of river beds at relatively high spatial resolution (0·01 to 1 m) through the extraction of digital elevation models (DEMs). The traditional approach to image capture has been to mount a metric camera to an aircraft, although non‐metric cameras have been mounted to a variety of novel aerial platforms to acquire river‐based imagery (e.g. helicopters, radio‐controlled motorized vehicles, tethered blimps and balloons). However, most of these techniques are designed to acquire imagery at flying heights above the riparian tree canopy. In relatively narrow channels (e.g. <20 m bankfull width), streamside trees can obscure the channel and limit continuous photogrammetric data acquisition of both the channel bed and banks, while still providing useful information regarding the riparian canopy and even spot elevations of the channel. This paper presents a technique for the capture and analysis of close‐range photogrammetric data acquired from a vertically mounted non‐metric camera suspended 10 m above the channel bed by a unipod. The camera is positioned under the riparian forest canopy so that the channel bed can be imaged without obstruction. The system is portable and permits relatively rapid image acquisition over rough terrain and in dense forest. The platform was used to generate DEMs with a nominal ground resolution of 0·03 m. DEMs generated from this platform required post‐possessing to either adjust or eliminate erroneous cells introduced by the extraction process, overhanging branches, and by the effects of refraction at the air–water interface for submerged portions of the channel bed. The vertical precision in the post‐processed surface generally ranged from ± 0·01 to 0·1 m depending on the quality of triangulation and the characteristics of the surface being imaged. Copyright © 2010 John Wiley & Sons, Ltd.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".