An Assessment of Temporal Decorrelation Compensation Methods for Forest Canopy Height Estimation Using Airborne L-Band Same-Day Repeat-Pass Polarimetric SAR Interferometry
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Bibliographic record
Abstract
We assess and compare several algorithms to compensate for temporal decorrelation observed in repeat-pass L-band polarimetric interferometric synthetic aperture radar (PolInSAR) measurements of forest canopy height. The analysis is performed on data acquired with an approximately 45-min temporal baseline using the uninhabited aerial vehicle synthetic aperture radar collected in August 2009 over temperate and boreal forests of the U.S. state of Maine and the Canadian province of Québec. This investigation presents several compensation methods based on the classical random volume over ground model, which include fixing the value of the extinction parameter, fixing the temporal decorrelation magnitude, or varying temporal decorrelation estimates with height. We also compare results with the random motion over ground model. While these methods have been presented in the literature previously, a comparison of the different methods and an assessment of their height estimation accuracy applied to the same datasets have not yet been performed. In addition, we introduce the use of ancillary reference forest height data from airborne large footprint lidar to estimate model parameters and to mitigate solution ambiguities. We finally demonstrate that this mitigation strategy is robust and suitable for use with future spaceborne lidar missions such as the Global Ecosystems Dynamics Investigation. The resulting PolInSAR canopy height estimates correspond well with those obtained from coincident field and airborne lidar data. Height estimation differences of 3.4 m (RMSE) were observed between the PolInSAR- and lidar-derived canopy height maps when using the fixed extinction method. These can be partially attributed to inherent differences in the sensor spatial resolutions and geolocation accuracy. The RMS error between the PolInSAR height estimates and the field collected Lorey's heights was 2.4 m.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it