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Patrice Carbonneau
Researcher at Durham University
Publications - 61
Citations - 3632
Patrice Carbonneau is an academic researcher from Durham University. The author has contributed to research in topics: Photogrammetry & Digital elevation model. The author has an hindex of 25, co-authored 61 publications receiving 3056 citations. Previous affiliations of Patrice Carbonneau include Institut national de la recherche scientifique & Université du Québec.
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Journal ArticleDOI
Topographic structure from motion: a new development in photogrammetric measurement
Mark A. Fonstad,James T. Dietrich,Brittany C. Courville,Jennifer L. R. Jensen,Patrice Carbonneau +4 more
TL;DR: This test shows that SfM and low-altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels.
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Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry
TL;DR: In this article, a rotary-winged Unmanned Aerial System (UAS) was used to produce digital elevation models (DEMs) with hyperspatial resolutions (c.10m to a few hundredm).
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Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry
TL;DR: It is argued that direct georeferencing and low-cost sUAS are capable of producing reliable topography products without recourse to expensive survey equipment and could transform survey practices in both academic and commercial disciplines.
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Making riverscapes real
TL;DR: In this paper, the authors apply the newly developed Fluvial Information System which integrates a suite of cutting edge, high-resolution, remote sensing methods in a spatially explicit framework.
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Catchment-scale mapping of surface grain size in gravel bed rivers using airborne digital imagery
TL;DR: In this article, the authors developed and assessed two methods for estimating median surface grain sizes using digital image processing from centimeter-resolution airborne imagery, and combined with field calibration measurements to establish predictive relationships for grain size as a function of both local image texture and local image semivariance.