Canopy Height Model (CHM) Derived From a TanDEM-X InSAR DSM and an Airborne Lidar DTM in Boreal Forest
Bibliographic record
Abstract
The first global X-band spaceborne single-pass interferometer mission, TanDEM-X, provides a spatially continuous map of global canopy elevations. In this paper, we assess the use of TanDEM-X data, in combination with an external digital terrain model (DTM), to map boreal canopy heights. A comparison of the TanDEM-X canopy height model (CHM) to a validated reference lidar CHM was performed based on two definitions of canopy height: canopy surface height (CSH) and dominant height (DH) at spatial resolutions ranging from 5 to 25 m, and at stand level. We found the TanDEM-X CHM to have a coarser resolution than the corresponding lidar CHM. This was apparent in the height validation of the TanDEM-X CHM, which had a RMSE of 2.7 m at the 5-m resolution, 1.9 m at the 25-m resolution, and 1.5 m at stand level. The height differences between the InSAR and lidar surfaces varied between 1.3 and 1.5 m, but InSAR heights were below the height of dominant trees by 4.6-7.5 m. Similar discrepancies were observed for the lidar CSH relatively to DH (6.04, 8.98, and 8.05 m, respectively). The results show that the TanDEM-X interferometric heights are very close to the lidar reference height and that penetration below the DH is caused by propagation of the microwave signal between the tree apices and the main foliage surface in boreal forest. Finally, the accuracy of InSAR height estimates was not sensitive to tree density effects, but was moderately affected by local incidence angles (LIAs), gap volume, and canopy height.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".