An advanced photogrammetric method to measure surface roughness: Application to volcanic terrains in the Piton de la Fournaise, Reunion Island

Bretar, Frédéric; Arab-Sedze, Mélanie; Champion, Jean; Pierrot-Deseilligny, Marc; Heggy, E.; Jacquemoud, Stéphane

doi:10.1016/j.rse.2013.03.026

Cited by 71 publications

(73 citation statements)

References 32 publications

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“…The high degree of possible user-software interaction, which can be very advantageous to adopt the 3-D reconstruction to each specific case study, might also be its drawback because further knowledge into the method is required. Only a few studies have used the software in geoscientific investigations (Bretar et al, 2013;Stumpf et al, 2014;Ouédraogo et al, 2014;Stöcker et al, 2015;Eltner and Schneider, 2015).…”

Section: Tools For Sfm Photogrammetry and Data Post-processingmentioning

confidence: 99%

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Image-based surface reconstruction in geomorphometry – merits, limits and developments

et al. 2016

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Abstract. Photogrammetry and geosciences have been closely linked since the late 19th century due to the acquisition of high-quality 3-D data sets of the environment, but it has so far been restricted to a limited range of remote sensing specialists because of the considerable cost of metric systems for the acquisition and treatment of airborne imagery. Today, a wide range of commercial and open-source software tools enable the generation of 3-D and 4-D models of complex geomorphological features by geoscientists and other non-experts users. In addition, very recent rapid developments in unmanned aerial vehicle (UAV) technology allow for the flexible generation of high-quality aerial surveying and ortho-photography at a relatively low cost.The increasing computing capabilities during the last decade, together with the development of highperformance digital sensors and the important software innovations developed by computer-based vision and visual perception research fields, have extended the rigorous processing of stereoscopic image data to a 3-D point cloud generation from a series of non-calibrated images. Structure-from-motion (SfM) workflows are based upon algorithms for efficient and automatic orientation of large image sets without further data acquisition information, examples including robust feature detectors like the scale-invariant feature transform for 2-D imagery. Nevertheless, the importance of carrying out well-established fieldwork strategies, using proper camera settings, ground control points and ground truth for understanding the different sources of errors, still needs to be adapted in the common scientific practice.This review intends not only to summarise the current state of the art on using SfM workflows in geomorphometry but also to give an overview of terms and fields of application. Furthermore, this article aims to quantify already achieved accuracies and used scales, using different strategies in order to evaluate possible stagnations of current developments and to identify key future challenges. It is our belief that some lessons learned from former articles, scientific reports and book chapters concerning the identification of common errors or "bad practices" and some other valuable information may help in guiding the future use of SfM photogrammetry in geosciences.

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Section: Tools For Sfm Photogrammetry and Data Post-processingmentioning

confidence: 99%

“…James and Robson (2012) acquired information on volcanic dome volume and structural variability prior to an eruption from multi-temporal imagery taken from a light aeroplane. Another interesting work by Bretar et al (2013) successfully reveals roughness differences in volcanic surfaces from lapilli deposits to slabby pahoehoe lava.…”

Section: Volcanologymentioning

confidence: 99%

Image-based surface reconstruction in geomorphometry – merits, limits and developments

et al. 2016

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“…It is widely recognized that L is not scale invariant, and therefore, the values obtained are dependent on profile length and plot size [47][48][49]. Zribi and Dechambre [50] stated that neglecting L will result in large errors in estimating radar backscatter, and therefore, they proposed a new measure, called Zs, which incorporates a slope effect.…”

Section: Effect Of Shellfish Species and Cover On Surface Roughnessmentioning

confidence: 99%

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Nieuwhof

Herman

Dankers³

et al. 2015

Remote Sensing

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On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potential of X-(TerraSAR-X) and C-band (Radarsat-2) dual-polarized SAR data to map shellfish densities, species and coverage. We investigated two backscatter models (the integral equation model (IEM) and Oh's model) for inversion possibilities. Surface roughness (vertical roughness RMSz and correlation length L) was measured of bare sediments and shellfish beds, which was then linked to shellfish density, presence and species. Oysters, mussels and bare sediments differed in RMSz, but because the backscatter saturates at relatively low RMSz values, it was not possible to retrieve shellfish density or species composition from X-and C-band SAR. Using a classification based on univariate and multivariate logistic regression of the field and SAR image data, we constructed maps of shellfish presence (Kappa statistics for calibration 0.56-0.74 for dual-polarized SAR), which were compared with independent field surveys of the contours of the beds (Kappa statistics of agreement 0.29-0.53 when using dual-polarized SAR). We conclude that spaceborne SAR allows one to monitor the contours of shellfish-beds (thus, distinguishing shellfish substrates from bare sediment and dispersed single shellfish), but not densities and species. Although OPEN ACCESSRemote Sens. 2015, 7 3711 spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency.

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“…Direct ground field measurement of surface roughness can be performed by either contact techniques [44][45][46] or non-contact techniques such as close range photogrammetry [15,26,[47][48][49][50] or laser scanning [32,[51][52][53]. Some authors made comparative studies [30,[54][55][56] and a review of the measurement techniques can be found in [3].…”

Section: Introductionmentioning

confidence: 99%

“…Some authors made comparative studies [30,[54][55][56] and a review of the measurement techniques can be found in [3]. Although close range photogrammetry and laser scanning enable retrieving a 3D digital elevation model (DEM) of the soil surface, many authors still carry on characterizing soil surface roughness by extracting 1D profiles on the 3D measurement [50,[57][58]. This can be explained by the fact that 1D statistical indices are widely spread and serve as reference, since measurement of 1D profiles was developed in the first place.…”

Section: Introductionmentioning

confidence: 99%

Statistical Characterization of Bare Soil Surface Microrelief

Vannier¹,

Taconet²,

Dusséaux³

et al. 2014

Advanced Geoscience Remote Sensing

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Because the soil surface occurs at the boundary between the atmosphere and the pedosphere, it plays an important role for geomorphologic processes. Roughness of soil surface is a key parameter to understand soil properties and physical processes related to substrate movement, water infiltration or runoff, and soil erosion. It has been noted by many authors that most of the soil surface and water interaction processes have characteristic lengths in millimeter scales. Soil irregularities at small scale, such as aggregates, clods and interrill depressions, influence water outflow and infiltration rate. They undergo rapid changes caused by farming imple‐ ments, followed by a slow evolution due to rainfall events. Another objective of soil surface roughness study is investigating the effects of different tillage implements on soil physical properties (friability, compaction, fragmentation and water content) to obtain an optimal crop emergence. Seedbed preparation focuses on the creation of fine aggregates and the size distribution of aggregates and clods produced by tillage operations is frequently measured. Active microwave remote sensing allows potential monitoring of soil surface roughness or moisture retrieving at field scale using space-based Synthetic Aperture Radars (SAR) with high spatial resolution (metric or decametric). The scattering of microwaves depends on several surface characteristics as well as on imagery configuration. The SAR signal is very sensitive to soil surface irregularities and structures (clod arrangement, furrows) and moisture content in the first few centimeters of soil (depending on the radar wavelength). In order to link the remote sensing observations to scattering physical models as well as for modelling purpose, key features of the soil microtopography should be characterized. However, this characteri‐ zation is not fully understood and some dispersion of roughness parameters can be observed in the same field according to the methodology used. It seems also, that when describing surface roughness as a whole, some information related to structured elements of the micro‐ topography is lost

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An advanced photogrammetric method to measure surface roughness: Application to volcanic terrains in the Piton de la Fournaise, Reunion Island

Cited by 71 publications

References 32 publications

Image-based surface reconstruction in geomorphometry – merits, limits and developments

Image-based surface reconstruction in geomorphometry – merits, limits and developments

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Statistical Characterization of Bare Soil Surface Microrelief

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