Unmanned aerial systems for precision forest inventory purposes: A review and case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned Aerial Systems (UAS) are capable of improving the efficiency of acquisition and providing fine spatial scale data for sustainable resource management. In this paper we begin by describing differences between UAS airframes, their successes and limitations, and list contemporary research applications. UAS compatible sensor technologies are discussed, including passive and active sensors. Finally, we detail a case study where UAS updated an Enhanced Forest Inventory (EFI) for a study area in interior British Columbia. Airborne Laser Scanning (ALS) from 2013 and Digital Aerial Photogrammetric (DAP) point clouds acquired using a UAS from 2015 were used to estimate individual tree height and volume increments. A total of 246 trees were detected using Canopy Height Models (CHMs) with 70% of these trees being matched in the ALS and DAP data sets. Mean tree growth between 2013 and 2015 from the CHM and 95th percentile of height (P95) was estimated at 0.68 ± 0.05 and 0.50 m ± 0.05 m, respectively. Similarly, mean gross tree volume increments (m3) were computed as 0.05 m3 ± 0.005 m3 and 0.03 m3 ± 0.005 m3 for the CHM and P95, respectively. The results indicate that information from UAS-DAP point clouds can generate spatially and temporally accurate inventories and have potential to inform a number of sustainable forest management activities.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it