Investigating ALOS PALSAR interferometric coherence in central Siberia at unfrozen and frozen conditions: implications for forest growing stock volume estimation
Bibliographic record
Abstract
AbstractThis paper investigates the impact of freezing on the properties of the magnitude of the interferometric coherence |γ| in central Siberia and discusses its implications for forest growing stock volume (GSV) estimation in the boreal zone. Eighty-seven acquisitions were employed and approximately 300 interferograms were generated. Accordingly, a high statistical credibility of the obtained results can be presumed. The temporal baselines of the interferograms ranged from 46 days to 2.5 years. The random volume over ground model was applied to support the interpretation of the observations. Compared with unfrozen conditions, we observed increased coherence over open areas, decreased coherence over dense forest, decreased spread of coherence, and improved correlation between |γ| and GSV during a frozen state. At frozen conditions, the experimental data showed no proof that the perpendicular baseline B⊥ impacted |γ| over dense forest, whereas at unfrozen conditions an impact was detected. Consequently, at frozen conditions, the temporal decorrelation was the major source of decorrelation and obstructed the detection of volume decorrelation effects. Nevertheless, |γ| acquired at frozen state exhibits potential for GSV mapping. The relationship between GSV and |γ| can be described with an average coefficient of determination R2 of 0.6. Saturation occurs at about 250 m3/ha.Cet article étudie l'impact du gel sur les propriétés de la cohérence interférométrique |γ| en Sibérie centrale (Russie) et discute son influence sur l'estimation du volume de bois sur pied (GSV). Pour ce faire, 87 acquisitions sont employées et environ 300 interférogrammes sont calculés. Au vu du nombre de données disponibles, des résultats d'une grande fiabilité peuvent être attendus. Les données sont caractérisées par une baseline temporelle allant de 46 jours à 2,5 ans. Le modèle «random volume over ground» est utilisé afin de faciliter l'interprétation des observations. En comparaison aux conditions non gelées, en état de gel nous observons une augmentation de la cohérence dans les espaces ouverts, une diminution de la cohérence dans les forêts dense, une moins forte dispersion de la cohérence et une meilleure corrélation entre la cohérence et le GSV. En situation de gel, les données expérimentales n'ont montré aucune influence de B⊥ sur la cohérence dans les forêts denses, alors qu'en conditions non gelées, un impact a pu être observé. Ainsi, en conditions de gel, la décorrélation temporelle est la principale source de décorrélation et masque les effets liés à la décorrélation de volume. Néanmoins, |γ| mesuré en conditions de gel semble montrer un certain potentiel pour la cartographie du GSV en Sibérie. La relation entre le GSV et |γ| a obtenu dans ces conditions un coefficient de détermination moyenné R2 de 0,6. La saturation quant à elle s'est produite à environ 250 m3/ha. AcknowledgementsThis work has been undertaken [in part] within the framework of the JAXA Kyoto and Carbon Initiative. ALOS PALSAR data were provided by JAXA EORC. The authors would like to thank the reviewers for their valuable comments and recommendations. Further thanks go to Nicole Richter and Christian Berger for thorough proofreading and suggestions.
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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 | 0.000 |
| 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 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".