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AIRBORNE X-HH INCIDENCE ANGLE IMPACT ON CANOPY HEIGHT RETREIVAL: IMPLICATIONS FOR SPACEBORNE X-HH TANDEM-X GLOBAL CANOPY HEIGHT MODEL

2012· article· en· W1975561768 on OpenAlexaff
M. Lorraine Tighe, Douglas J. King, Heiko Balzter, A. Bannari, Heather McNairn

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2012
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of OttawaCarleton University
Fundersnot available
KeywordsInterferometric synthetic aperture radarCanopyRemote sensingVegetation (pathology)Synthetic aperture radarEnvironmental scienceShuttle Radar Topography MissionTree canopySatelliteRadarRange (aeronautics)MeteorologyGeographyDigital elevation modelComputer science

Abstract

fetched live from OpenAlex

Abstract. To support international climate change mitigation efforts, the United Nations REDD+ initiative (Reducing Emissions from Deforestation and Degradation) seeks to reduce land use induced greenhouse gas emissions to the atmosphere. It requires independent monitoring of forest cover and forest biomass information in a spatially explicit form. It is widely recognised that remote sensing is required to deliver this information. Synthetic Aperture Radar interferometry (InSAR) techniques have gained traction in the last decade as a viable technology from which vegetation canopy height and bare earth elevations can be derived. The viewing geometry of a SAR sensor is side-looking where the radar pulse is transmitted out to one side of the aircraft or satellite, defining an incidence angle (θ) range. The incidence angle will change from near-range (NR) to far-range (FR) across of the track of the SAR platform. InSAR uses image pairs and thus, contain two set of incidence angles. Changes in the InSAR incidence angles can alter the relative contributions from the vegetation canopy and the ground surface and thus, affect the retrieved vegetation canopy height. Incidence angle change is less pronounced in spaceborne data than in airborne data and mitigated somewhat when multiple InSAR-data takes are combined. This study uses NEXTMap® single- and multi-pass X-band HH polarized InSAR to derive vegetation canopy height from the scattering phase centre height (hspc). Comparisons with in situ vegetation canopy height over three test sites (Arizona-1, Minnesota-2); the effect of incidence angle changes across swath on the X-HH InSAR hspc was examined. Results indicate at steep incidence angles (θ = 35º), more exposure of lower vegetation canopy structure (e.g. tree trunks) led to greater lower canopy double bounce, increased ground scattering, and decreased volume scattering. This resulted in a lower scattering phase centre height (hspc) or a greater underestimation of vegetation canopy height given by the single-pass X-HH InSAR data. The opposite effect occurs in the far range (θ = 55º), an increase in volume scattering resulted in more accurate vegetation canopy heights when compared to in situ measurements. These findings indicate that incidence angle corrections should be applied to airborne X-HH single-pass InSAR. In contrast, NEXTMap X-HH (multi-pass data) hspc data experienced little or no effect of incidence angle, possibly because NEXTMap is an aggregation of multi-pass flight line strips, which averages data over several incidence angles. These results may aid in the understanding of potential incidence angle effects in Astrium spaceborne Tandem-X data, which will have global digital surface elevation coverage by 2015.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.275
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2012
Admission routes1
Has abstractyes

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Same venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesSame topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207