Radar Forest Height Estimation in Mountainous Terrain Using Tandem-X Coherence Data
Bibliographic record
Abstract
In this paper, we consider the problem of radar estimation of forest canopy height in regions with dense forests and severe topography. We combine a reference digital elevation model with multiple satellite baselines from ascending and descending orbits to develop a merging algorithm relating single pass interferometric coherence to forest canopy height. We first describe the algorithm and processing steps used for height estimation and then apply the technique to a mountainous study site in British Columbia, Canada, using data from the Tandem-X satellite pair. We devise a new masking scheme to isolate potential problem areas in sloped terrain and apply the new merging algorithm by using multiple Tandem-X tracks to overcome the gaps left due to the masking procedure. The radar height products are validated by using a network of ground forest measurement sites and supporting lidar. The regression statistics show an r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.70 and rmse of 4.1 m between the radar and the field measured heights. By examining height errors, we implement a new test for the presence of canopy extinction, or subcanopy surface scattering, and demonstrate that in the dense and mountainous forests of British Columbia, there are significant canopy extinction effects in X-band imagery.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".