Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
Bibliographic record
Abstract
Remote sensing (RS) technologies, particularly light detection and ranging (LiDAR) and multispectral (MS) imagery, provide broad-scale vegetation monitoring capabilities at varying spatial resolutions. Remotely piloted aircraft systems (RPAS) equipped with LiDAR and MS sensors can enhance vegetation assessments by offering flexible flight schedules and capturing fine-resolution data. Further integration of deep learning (DL) models holds promise for automating the post-processing workflow, which is particularly important for vegetation monitoring applications. This protocol outlines a suite of practical methods for collecting, processing, aligning, and merging RPAS-based LiDAR and MS data for individual 3D tree delineation using an interactive DL plugin. The proprietary DL model effectively detects and segments tree boundaries across various sensors, study sites, and data resolutions within forest ecosystems. Our specific application and motivation for developing this protocol and tool is for monitoring forest recovery on reclaimed oil and gas wellsites. Currently field-based assessment methods are time-consuming, labor-intensive, and spatially limited. As reclamation efforts expand, there is a growing need for more efficient, and scalable approaches to monitor reclamation success and ecosystem recovery. By advancing RPAS-based DL applications, this research supports the monitoring of ecological recovery on reclaimed wellsites and is also applicable to other forested landscapes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".