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ISIIS-DPI Plankton Imager

The ISIIS Deep-focus Particle Imager is a telecentric shadowgraph camera system made to image small particles (such as plankton, marine snow, oil droplets or water spray) over a large depth of field.

The are extremely relevant to study plankton, in-situ, as they are able to "scan" large volume of water at once, while providing sharp outlines of organisms and their internal structures.

The camera system is composed of two enclosures facing each other: the light source enclosure and the camera enclosure. The distance between these two housings often represents the full depth of field the camera is able to image while maintaining a good focus.

We offer three standard sizes of ISIIS-DPI imagers, named after the diameter of the plano-convex field lenses used in their optics: 75mm, 125mm and 150mm.

Depending on your applications, we provide the imager as a stand-alone, thanks to our SIDEKICK data handler pod, or we pair it with one or our telemetry unit so it can be used on a tethered system, such as onboard one of our underwater towed vehicles.


ISIIS-DPI Configuration Examples

ISIIS-DPI Configuration.png

Scientists have stacked imagers to sample a create a large field of view while maintaining high resolution, or used a combination of two cameras, each focusing on different size taxa.

ISIIS-DPI imagers are well suited to image any "particles" 300um and up. Depth of field will vary. A 1mm particle will keep an acceptable definition over 30cm depth of field. A 300um particles will be hard to recognize if the depth of field is set to be more than 5cm.

The first ISIIS imager was developed in 2005. Since then, many missions, with different ISIIS units have been completed.

Deployments in different environments (from eutrophic murky waters to oligotrophic seas), sea-state, and from various supporting platforms, prove the instrument is highly capable and versatile.



What is it?

ISIIS plankton imagers are based on a very specific back-illumination imaging technique. It is a focused shadowgraph system that captures silhouettes of plankton. It uses a pseudo collimated beam of light ensuring projections/shadows of organisms that are telecentric: the size of an object is not dependent on their position within the field of view.

What’s the main advantage of a line-scan camera?

A line scan camera takes a continuous image and therefore you need not worry about overlapping images (counting taxa twice for example) or subsampling. The line-scan camera also allows us to use the entire enclosure viewport diameter as opposed to fitting a “square image” inside a viewport. If you match the scanning rate of the camera with the water speed flowing in between the pod you obtain a true image, neither stretched nor compressed.

What does ISIIS-DPI stand for?

ISIIS (In-Situ Ichthyoplankton Imaging System) was first developed to image large volumes of water to survey ichthyoplankton patchiness in the Gulf Stream off the coast of Florida, back in 2005. Since the imager strength is its ability to image over a large depth of field, and since it is capable of imaging more than plankton, we added DPI to its name, for Deep-focus Particle Imager. ISIIS is still the most referred name in published literature.

What about motion blur?

The light enclosure shines lots of light onto the camera, and therefore we are always very close to the smallest exposure time allowed by the camera sensor.  Motion blur has never really been an issue thus far. As we develop the technology for smaller than 100um particles, it may become another challenge to address.

What kind of camera do you use?

We use industrial cameras, with either a USB3 or GigE interface. A line-scan camera is ideal for application with constant speed in the 5 knots range. An area-scan camera allows the user to deploy at highly variable tow speeds, do vertical profiles or stationary set-ups like moorings.

Do you provide analysis services?

We do not. However, several scientists have open-source codes and are happy to collaborate or share their experience.We can also recommend private entities to develop custom algorithms for your application.

Bellamare is working on releasing flat-fielding and segmentation solutions.

Can you explain pixel-resolution?

Pixel resolution is a function of the number of pixels available on the camera sensor and the size of the image projected onto that sensor. A sensor with 2440 pixels across the width and 2048 pixels high has a resolution of 2048x2440. If the projected image is 12cm high, each pixel of the sensor captures 120,000um / 2048 = 58.6um / pixel.

You need at least 10 to 15 pixels to start representing an object adequately . Such a camera resolution would therefore be acceptable to capture taxa that is about 700um in size or larger

How do I calculate the volume of water imaged per second?

This comes down to simple maths.
If your boat speed is 5 knots (2.57m/sec), then the water volume imaged per second, for a line-scan camera is, using the 150mm viewport example: 2.57 (Speed) x 1000 * 135 (FOV) x 300 (DOF) = 104 L /sec

For an area-scan camera, with our 75mm viewport example, it would be: 42 (FOV) x 42 (FOV) x 30 (DOF) x frame rate per second of the camera

What about depth of field?

We found that identification of organisms larger than 1 mm can be done over 30cm depth of field. However, if your interest is in smaller taxa, in the 300 to 500μm range, you will probably limit the depth of field to 3cm (really good) up to perhaps 5cm maximum.

Contact us if your area of interest in around 100um or smaller. However Depth of field will quite limited: 1cm or less.

What about imaging different size taxa altogether?

To tackle this issue, several groups have used two imagers side by side. One imager would focus on smaller organisms over a shallow depth of field while the other would image much larger taxa over a larger depth of field.

Another approach has been to stack imagers. For example, stacking high resolution shadowgraph on top of each other allows to capture large tax while maintaining a high resolution. 

Tim_s (7).JPG


This is an effort to collect publicaons using the ISIIS DPI, to date. Please let us know if we inadvertently omitted yours as we welcome your help to keep this list up to date.

Last Updated: March 2020

  • Cowen RK and Guigand CM (2008) In situ Ichthyoplankton Imaging System (ISIIS): System design and preliminary results. Limnol Oceanogr-Meth 6:126-132

  • Cowen RK, Greer AT, Guigand CM, Hare JA, Richardson DE, Walsh HJ (2013) Evaluaon of the in situ ichthyoplankton imaging system (ISIIS): Comparison with the tradional (bongo net) sampler. Fish Bull 111:1-12

  • Dzwonkowski B, Greer AT, Briseño-Avena C, Krause JW, Soto IM, Hernandez FJ, Deary AL, Wiggert JD, Joung D, Fitzpatrick PJ, and others (2017) Estuarine influence on biogeochemical properes of the Alabama shelf during the fall season. Cont Shelf Res 140:96-109

  • Dzwonkowski B, Fournier S, Reager JT, Milroy S, Park K, Shiller AM, Greer AT, Soto I, Dykstra SL, Sanial V (2018) Tracking sea surface salinity and dissolved oxygen on a river-influenced, seasonally strafied shelf, Mississippi Bight, northern Gulf of Mexico. Cont Shelf Res 169:25-33

  • Failleaz R, Picheral M, Luo JY, Guigand C, Cowen RK, Irisson J (2016) Imperfect automac image classificaon successfully describes plankton distribuon paerns. Methods in Oceanography 15-16:60-77

  • Greer AT (2018) In-situ shadowgraph imaging. Mar Technol Soc J 52:62-65

  • Greer AT, Boyee AD, Cruz VJ, Cambazoglu MK, Dzwonkowski B, Chiaverano LM, Dykstra SL, Briseno-Avena C, Cowen RK, Wiggert JD (in press) Contrasng fine-scale distribuonal paerns of zooplankton driven by the formaon of a diatom-dominated thin layer. Limnol Oceanogr

  • Greer AT, Briseno-Avena C, Deary AL, Cowen RK, Hernandez FJ, Graham WM (2017) Associaons between lobster phyllosoma and gelanous zooplankton in relaon to oceanographic properes in the northern Gulf of Mexico. Fisheries Oceanography 26:693-704

  • Greer AT, Cowen RK, Guigand CM, McManus MA, Sevadjian JC, Timmerman AHV (2013) Relaonships between phytoplankton thin layers and the fine-scale vercal distribuons of two trophic levels of zooplankton. J Plankton Res 35:939-956

  • Greer AT, Shiller AM, Hofmann EE, Wiggert JD, Warner SJ, Parra SM, Pan C, Book JW, Joung D, Dykstra S, and others (2018) Funconing of coastal river-dominated ecosystems and implicaons for oil spill response: From observaons to mechanisms and models. Oceanography 31:90-103

  • Greer AT and Woodson CB (2016) Applicaon of a predator - prey overlap metric to determine the impact of sub-grid scale feeding dynamics on ecosystem producvity. ICES J Mar Sci 73:1051-1061

  • Greer AT, Chiaverano LM, Diy JG, Hernandez FJ (2019) In situ observaons of fish larvae encased within a pelagic gelanous matrix. Mar Ecol Prog Ser 614:209-214

  • Greer AT, Woodson CB, Guigand CM, Cowen RK (2016) Larval fishes ulize Batesian mimicry as a survival strategy in the plankton. Mar Ecol Prog Ser 551:1-12

  • Greer AT, Cowen RK, Guigand CM, Hare JA (2015) Fine-scale planktonic habitat paroning at a shelf-slope front revealed by a high-resoluon imaging system. J Mar Syst 142:111-125

  • Greer AT, Chiaverano LM, Luo JY, Cowen RK, Graham WM (2018) Ecology and behaviour of holoplanktonic scyphomedusae and their interacons with larval and juvenile fishes in the northern Gulf of Mexico. ICES J Mar Sci 75:751-763

  • Greer AT, Woodson CB, Smith CE, Guigand CM, Cowen RK (2016) Examining mesozooplankton patch structure and its implicaons for trophic interacons in the northern Gulf of Mexico. J Plankton Res 38:1115-1134

  • Greer AT, Cowen RK, Guigand CM, Hare JA, Tang D (2014) The role of internal waves in larval fish interacons with potenal predators and prey. Prog Oceanogr 127:47-61

  • Luo JY, Irisson JO, Graham B, Guigand C, Sarafraz A, Mader C, Cowen RK (2018) Automated plankton image analysis using convoluonal neural networks. Limnol Oceanogr-Meth 16:814-827

  • Luo JY, Grassian B, Tang D, Irisson JO, Greer AT, Guigand CM, McClatchie S, Cowen RK (2014) Environmental drivers of the fine-scale distribuon of a gelanous zooplankton community across a mesoscale front. Mar Ecol Prog Ser 510:129-149

  • McClatchie S, Cowen R, Nieto K, Greer A, Luo JY, Guigand C, Demer D, Griffith D, Rudnick D (2012) Resoluon of fine biological structure including small narcomedusae across a front in the southern California Bight. Journal of Geophysical Research C: Oceans 117

  • Parra SM, Greer AT, Book JW, Deary AL, Soto IM, Culpepper C, Hernandez FJ, Miles TN (2019) Acousc detecon of zooplankton diel vercal migraon behaviors on the northern Gulf of Mexico shelf. Limnol Oceanogr 64:2092-2113

  • Robinson KL, Luo JY, Sponaugle S, Guigand CM, Cowen RK (2017) A tale of two crowds: Public engagement in plankton classificaon. Froners in Marine Science 4:82

  • Schmid MS, Cowen RK, Robinson K, Luo JY, Briseño-Avena C, Sponaugle S (2020) Prey and predator overlap at the edge of a mesoscale eddy: Fine-scale, in-situ distribuons to inform our understanding of oceanographic processes. Sci Rep 10:921

  • Sevadjian JC, McManus MA, Ryan J, Greer AT, Cowen RK, Woodson CB (2014) Across-shore variability in plankton layering and abundance associated with physical forcing in Monterey Bay, California. Cont Shelf Res 72:138-151

  • Timmerman AHV, McManus MA, Cheriton OM, Cowen RK, Greer AT, Kudela RM, Ruenberg K, Sevadjian J (2014) Hidden thin layers of toxic diatoms in a coastal bay. Deep-Sea Res Pt II 101:129-140

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