Accuracy of optical image analysis compared to conventional vegetation measurements for estimating morphological features of emergent vegetation

Lemein, Todd; Cox, Daniel T.; Albert, Dennis A.; Mori, Nobuhito

doi:10.1016/j.ecss.2014.12.051

Cited by 12 publications

(9 citation statements)

References 33 publications

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“…0.2-m-wide) region of saltmarsh vegetation is isolated while exposed at low tide, a vertical backing board is placed behind the vegetation, and photographs of the horizontal view of the vegetation are analyzed. The percent of the backing board obscured by vegetation, here denoted B, has been related to biomass Lemein et al (2015); Möller (2006);Neumeier (2005). Within a sedge canopy, a strong correlation has also been found between B and N dh (Lemein et al, 2015), where h = stem length (for a uniform-height canopy with depth-independent d, N dh is the depth-integral of a).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

“…The percent of the backing board obscured by vegetation, here denoted B, has been related to biomass Lemein et al (2015); Möller (2006);Neumeier (2005). Within a sedge canopy, a strong correlation has also been found between B and N dh (Lemein et al, 2015), where h = stem length (for a uniform-height canopy with depth-independent d, N dh is the depth-integral of a). Vegetation height has been estimated from depthdependence of B (Lemein et al, 2015;Möller, 2006).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

“…Vegetation height has been estimated from depthdependence of B (Lemein et al, 2015;Möller, 2006). Depth-dependence of a has not been inferred directly from photographs, although a profiles have been estimated by combining photogrammetric estimates of stem length with an empirical relationship between stem length and diameter (Lemein et al, 2015, such relationships likely depend on species and seasonality).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

“…For one approach (Lemein et al, 2015;Möller, 2006;Neumeier, 2005), a small (e.g. 0.2-m-wide) region of saltmarsh vegetation is isolated while exposed at low tide, a vertical backing board is placed behind the vegetation, and photographs of the horizontal view of the vegetation are analyzed.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

See 3 more Smart Citations

Efficient three-dimensional reconstruction of aquatic vegetation geometry: Estimating morphological parameters influencing hydrodynamic drag

Liénard

Lynn

Strigul

et al. 2016

Estuarine, Coastal and Shelf Science

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

See 2 more Smart Citations

Efficient three-dimensional reconstruction of aquatic vegetation geometry: Estimating morphological parameters influencing hydrodynamic drag

Liénard

Lynn

Strigul

et al. 2016

Estuarine, Coastal and Shelf Science

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“…Digital image analysis has been compared with visual disease assessment for several diseases such as coffee rust [ 24 ], powdery mildew [ 25 ], yellow rust [ 26 ] and citrus canker [ 27 ]. These studies indicated that colour or monochrome image analysis provided more accurate measurement, whilst drastically reducing the time required for examination [ 16 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Hulin

Brain

et al. 2015

Plant Methods

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BackgroundPseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.ResultsIn this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.ConclusionsAutomated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0100-8) contains supplementary material, which is available to authorized users.

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Simplification bias: lessons from laboratory and field experiments on flow through aquatic vegetation

Tinoco

Juan

Mullarney

2020

Earth Surf Processes Landf

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We present a critical analysis of experimental findings on vegetation-flow-sediment interactions obtained through both laboratory and field experiments on tidal and coastal environments. It is well established that aquatic vegetation provides a wide range of ecosystem services (e.g. protecting coastal communities from extreme events, reducing riverbank and coastal erosion, housing diverse ecosystems), and the effort to better understand such services has led to multiple approaches to reproduce the relevant physical processes through detailed laboratory experiments. State-of-the-art measurement techniques allow researchers to measure velocity fields and sediment transport with high spatial and temporal resolution under well-controlled flow conditions, yielding predictions for hydrodynamic and sediment transport scenarios that depend on simplified or bulk vegetation parameters. However, recent field studies have shown that some simplifications on the experimental setup (e.g. the use of rigid elements, a single diameter, a single element height, regular or staggered layout) can bias the outcome of the study, by either hiding or amplifying some of the relevant physical processes found in natural conditions. We discuss some observed cases of bias, including general practices that can lead to compromises associated with simplified assumptions. The analysis presented will identify potential pathways to move forward with laboratory and field measurements, which could better inform predictors to produce more robust, universal and accurate predictions on flow-vegetation-sediment interactions.

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Accuracy of optical image analysis compared to conventional vegetation measurements for estimating morphological features of emergent vegetation

Cited by 12 publications

References 33 publications

Efficient three-dimensional reconstruction of aquatic vegetation geometry: Estimating morphological parameters influencing hydrodynamic drag

Efficient three-dimensional reconstruction of aquatic vegetation geometry: Estimating morphological parameters influencing hydrodynamic drag

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Simplification bias: lessons from laboratory and field experiments on flow through aquatic vegetation

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