Evaluating the Impacts of Using Different Digital Surface Models to Estimate Forest Height with TanDEM-X Interferometric Coherence Data
Bibliographic record
Abstract
In our previous studies, we demonstrated the usefulness of TanDEM-X interferometric bistatic mode with single polarization to obtain forest heights for the purposes of large area mapping. A key feature of our approach has been the use of a simplified Random Volume Over Ground (RVOG) model that locally estimates forest height. The model takes TanDEM-X interferometric coherence amplitude as an input and uses an external Digital Surface Model (DSM) to account for local slope variations due to terrain topography in order to achieve accurate forest height estimation. The selection of DSM for use as a local slope reference is essential, as an inaccurate DSM will result in less accurate terrain-correction and forest height estimation. In this paper, we assessed TanDEM-X height estimates associated with scale variations in different DSMs used in the model over a remote sensing supersite in Petawawa, Canada. The DSMs used for assessments and comparisons included ASTER GDEM, ALOS GDSM, airborne DRAPE DSM, Canadian DSM and TanDEM-X DSM. Airborne Laser Scanning (ALS) data were used as reference for terrain slope and forest height comparisons. The results showed that, with the exception of the ASTER GDEM, all DSMs were sufficiently accurate for the simplified RVOG model to provide a satisfactory estimate of stand-level forest height. When compared to the ALS 95th height percentile, the modeled forest heights had R2 values greater than 80% and Root-Mean-Square Errors (RMSE) less than 2 m. For a close similarity in slope estimation with the ALS reference, coverage across Canada and open data access, the 0.75 arc-second (20 m) resolution Canadian DSM was selected as a preferred choice for the simplified RVOG model to provide TanDEM-X height estimation in Canada.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".