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Record W2169141284 · doi:10.5589/m05-016

Accuracy of an IFSAR-derived digital terrain model under a conifer forest canopy

2005· article· en· W2169141284 on OpenAlexvenueno aff
Hans‐Erik Andersen, Stephen E. Reutebuch, Robert J. McGaughey

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTerrainRemote sensingElevation (ballistics)Digital elevation modelCanopyMean squared errorInterferometric synthetic aperture radarEnvironmental sciencePhotogrammetrySynthetic aperture radarTree canopyGeographyCartographyMathematicsStatistics

Abstract

fetched live from OpenAlex

AbstractAccurate digital terrain models (DTMs) are necessary for a variety of forest resource management applications, including watershed management, timber harvest planning, and fire management. Traditional methods for acquiring topographic data typically rely on aerial photogrammetry, where measurement of the terrain surface below forest canopy is difficult and error prone. The recent emergence of airborne P-band interferometric synthetic aperture radar (IFSAR), a high-resolution, microwave remote sensing technology, has the potential to provide significantly more accurate terrain models in forested areas. Low-frequency, P-band radar energy physically penetrates through the vegetation canopy and reflects from the underlying terrain surface, allowing for accurate measurement of the terrain surface elevation even in areas with dense forest cover. In this study, the accuracy of a high-resolution DTM derived from P-band IFSAR data collected over a mountainous forest area in western Washington State was rigorously evaluated through a comparison with 347 topographic checkpoints measured with total station survey equipment and collected under a variety of canopy densities. The mean DTM error was –0.28 ± 2.59 m (mean ± standard deviation), and the root mean squared error (RMSE) was 2.6 m. DTM elevation errors for four canopy cover classes were –0.67 ± 1.20 m (RMSE = 1.38 m) for clearcut, –0.62 ± 1.00 m (RMSE = 1.18 m) for heavily thinned, –0.41 ± 2.32 m (RMSE = 2.36 m) for lightly thinned, and 0.20 ± 3.31 m (RMSE = 3.32 m) for uncut.Les modèles numériques d'altitude (MNA) précis sont nécessaires pour une variété d'applications dans la gestion des ressources forestières, incluant la gestion des bassins versants, la planification des opérations de coupe et la gestion des incendies de forêt. Les méthodes traditionnelles d'acquisition de données topographiques s'appuient généralement sur la photogrammétrie aérienne, où les mesures de la surface du terrain sous le couvert forestier est difficile et sujette aux erreurs. L'émergence récente des données interférométriques radar aéroporté à synthèse d'ouverture en bande P (IFSAR), une technologie de télédétection micro-onde à haute résolution, pourrait fournir des modèles d'altitude significativement plus précis dans les zones forestières. L'énergie à basse fréquence du radar en bande P pénètre physiquement à travers le couvert de végétation et est réfléchie par la surface sous-jacente du terrain permettant la mesure précise de l'altitude à la surface du terrain, même dans les zones de couvert forestier dense. Dans cette étude, la précision d'un MNA à haute résolution dérivé des données IFSAR en bande P acquises au-dessus d'une zone forestière montagneuse dans l'ouest de l'état de Washington a été évaluée de façon rigoureuse par le biais d'une comparaison avec 347 points de contrôle topographiques mesurés à l'aide d'équipement de levés par station totale et collectées pour une variété de densités de couvert. L'erreur moyenne au niveau du MNA était de –0,28 ± 2,59 m (moyenne ± ET) et l'erreur quadratique (RMSE) était de 2,6 m. Les erreurs d'altitude du MNA pour quatre classes de couvert étaient: coupes à blanc –0,67 ± 1,20 m (RMSE = 1,38 m), éclaircies fortes –0,62 ± 1,00 m (RMSE = 1,18 m), éclaircies légères –0,41 ± 2,32 m (RMSE = 2,36 m) et zones non coupées 0,20 ± 3,31 m (RMSE = 3,32 m).[Traduit par la Rédaction]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.240
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2005
Admission routes1
Has abstractyes

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