Effect of ground surface interpolation methods on the accuracy of forest attribute modelling using unmanned aerial systems-based digital aerial photogrammetry
Bibliographic record
Abstract
Unmanned aerial systems digital aerial photogrammetry (UAS-DAP) is an emerging technology that has the capacity to generate dense three-dimensional point clouds similar to airborne laser scanning (ALS). Over forested areas, these point clouds can be used to model forest attributes using the area-based approach (ABA). However, with DAP point clouds, canopy occlusion contributes to larger gaps in terrain registration from UAS-DAP compared to ALS point-clouds. Few studies have investigated the terrain modelling and forest inventory capacity of UAS-DAP over complex coniferous forests. In this study, we applied common terrain surface-interpolation routines using an established set of optimal UAS-DAP ground points and analysed how these routines influenced the prediction accuracy of forest stand attributes. Interpolation routines included inverse-distance weighted (IDW), natural neighbour (NATN), triangulated irregular network (TIN), and spline with tension (SPLT). The forest attributes of interest included mean tree height (Hmean), Lorey’s height (HLorey) and stem volume per hectare (Vstem). Models were developed using metrics calculated from the vertical distribution of the UAS-DAP point cloud normalized by the different UAS-DAP terrain surfaces in addition to a reference surface generated from commercially provided ALS ground points. Results showed no significant difference between predictions derived from different terrain surfaces for all three dependent variables; however, the IDW method produced a distribution of wall-to-wall predictions most similar to those from the ALS-DEM. The best performing forest attribute models for Hmean, HLorey and Vstem yielded mean RMSE values of 1.19 m (7.29%), 0.92 m (5.04%) and 54.55 m3 ha−1 (26.66%) respectively across the four UAS-DAP terrain surfaces generated. Model performance was higher yet comparable when using the ALS-DEM for point cloud height normalization with RMSE values of 0.73 m (4.43%), 0.59 m (3.24%) and 37.31 m3 ha−1 (18.24%).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".