Measuring coral reef terrain roughness using ‘Structure-from-Motion’ close-range photogrammetry

Leon, Javier X.; Roelfsema, Christiaan M; Saunders, Megan I.; Phinn, Stuart

doi:10.1016/j.geomorph.2015.01.030

Cited by 174 publications

(188 citation statements)

References 36 publications

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“…Patch level models were reconstructed using the software package Photoscan Professional v1.1.6 (AgiSoft LLC). Photoscan uses a similar workflow as VisualSFM to build 3D models based on overlapping photos of the same areas from different angles and has been used in other studies which have modeled larger areas of benthic marine habitat as done here [16,19]. This process is highly dependent on the quality of photos, overlap between photos, and capturing all surfaces from multiple angles.…”

Section: Patch-scalementioning

confidence: 99%

“…At very large spatial extents, variables such as bottom roughness and slope have been derived from airborne Lidar [10,11] and sub-surface acoustic profiling [12][13][14][15]. Over smaller areas the direct measurement of factors such as vertical relief, number of holes, surface roughness, and even fractal dimension [5,[16][17][18] have been commonly estimated by divers. Among these techniques there has always been a tradeoff between area covered and resolution obtained such that very precise and accurate methodologies were often only practical over very small areas.…”

Section: Introductionmentioning

confidence: 99%

“…Among these techniques there has always been a tradeoff between area covered and resolution obtained such that very precise and accurate methodologies were often only practical over very small areas. However, recent advances in image processing algorithms and computing power has seen a proliferation of techniques to estimate structural complexity for large reef areas based on digital photogrammetry and application of structure from motion software [16,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…Systems have been developed to successfully map and model large areas of benthic habitat (up to~500 m 2 ) [19,31,32] or even km long transects [23] with a variety of off-the-shelf to custom-built hardware and software packages. Photogrammetry-based models derived from these various processes have been used to look at the relative benefits of various metrics of structural complexity used to characterise benthic habitats [16,20].…”

Section: Introductionmentioning

confidence: 99%

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Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

et al. 2015

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Abstract:In tropical reef ecosystems corals are the key habitat builders providing most ecosystem structure, which influences coral reef biodiversity and resilience. Remote sensing applications have progressed significantly and photogrammetry together with application of structure from motion software is emerging as a leading technique to create three-dimensional (3D) models of corals and reefs from which biophysical properties of structural complexity can be quantified. This enables the addressing of a range of important marine research questions, such as what the role of habitat complexity is in driving key ecological processes (i.e., foraging). Yet, it is essential to assess the accuracy and precision of photogrammetric measurements to support their application in mapping, monitoring and quantifying coral reef form and structure. This study evaluated the precision (by repeated modeling) and accuracy (by comparison with laser reference models) of geometry and structural complexity metrics derived from photogrammetric 3D models of marine benthic habitat at two ecologically relevant spatial extents; individual coral colonies of a range of common morphologies and patches of reef area of 100s of square metres. Surface rugosity measurements were generally precise across all morphologies and spatial extents with average differences in the geometry of replicate models of 1-6 mm for coral colonies and 25 mm for the reef area. Precision decreased with complexity of the coral morphology, with metrics for small massive corals being the most precise (1% coefficient of variation (CV) in surface rugosity) and metrics for bottlebrush corals being the least precise (10% CV in surface rugosity). There was no indication however that precision was related to complexity for the patch-scale modelling. The 3D geometry of coral models differed by only 1-3 mm from laser reference models. However, high spatial variation in these differences around the model led to a consistent underestimation of surface rugosity values for all morphs of between 8% and 37%. This study highlights the utility of several off-the-shelf photogrammetry tools for the measurement of structural complexity across a range of scales relevant to ecologist and managers. It also provides important information on the accuracy and precision of these systems which should allow for their targeted use by non-experts in computer vision within these contexts.

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Section: Patch-scalementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

et al. 2015

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“…It is widely used in the research, i.e. (Gonçalves and Henriques, 2015;Leon et al, 2015;Javernick et al, 2014;Uysal et al, 2015), because of its solid performance and relatively efficient processing.…”

Section: Photogrammetric Uas Data Processingmentioning

confidence: 99%

Overland Flow Analysis Using Time Series of Suas-Derived Elevation Models

Jeziorska¹,

Mitášová²,

Petrášová³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.

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Morphological characterization of coral reefs by combining lidar and MBES data: A case study from Yuanzhi Island, South China Sea

Zhang

Yang

Zhang³

et al. 2017

JGR Oceans

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The correlation between seafloor morphological features and biological complexity has been identified in numerous recent studies. This research focused on the potential for accurate characterization of coral reefs based on high‐resolution bathymetry from multiple sources. A standard deviation (STD) based method for quantitatively characterizing terrain complexity was developed that includes robust estimation to correct for irregular bathymetry and a calibration for the depth‐dependent variablity of measurement noise. Airborne lidar and shipborne sonar bathymetry measurements from Yuanzhi Island, South China Sea, were merged to generate seamless high‐resolution coverage of coral bathymetry from the shoreline to deep water. The new algorithm was applied to the Yuanzhi Island surveys to generate maps of quantitive terrain complexity, which were then compared to in situ video observations of coral abundance. The terrain complexity parameter is significantly correlated with seafloor coral abundance, demonstrating the potential for accurately and efficiently mapping coral abundance through seafloor surveys, including combinations of surveys using different sensors.

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Measuring coral reef terrain roughness using ‘Structure-from-Motion’ close-range photogrammetry

Cited by 174 publications

References 36 publications

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

Accuracy and Precision of Habitat Structural Complexity Metrics Derived from Underwater Photogrammetry

Overland Flow Analysis Using Time Series of Suas-Derived Elevation Models

Morphological characterization of coral reefs by combining lidar and MBES data: A case study from Yuanzhi Island, South China Sea

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