We thank the Paul G. Allen Family Foundation for funding this research and are grateful to Ruth Gates for the inspiration to use technology to help conserve reefs. We also thank NOAA and other collaborators for thoughtful discussion concerning these methods. Lastly, we thank Catie Foley and Patrick Nichols for providing the drone and underwater video of these methods.
We acknowledge the National Fish and Wildlife Foundation as a funding partner in this work.

1. Materials
<ol>
	<li>Camera
	<ol>
		<li>Ensure minimum specifications of durability and waterproof nature (or a waterproof housing) and a minimum frame rate of 2 frames/s (fps). 
		NOTE: A minimum frame rate of ~4 fps was used in this example.</li>
		<li>Digital Single Lens Reflex (DSLR) camera
		<ol>
			<li>Set the camera to shoot continuously at a photo capture rate between 2 fps and 5 fps.</li>
			<li>To reproduce the protocol described for this example, use a camera in an underwater housing (see Table of Materials) with the following settings: Manual Mode (M); f10, 18 mm; shutter speed = 1/320; exposure compensation = -1/3; image quality = highest, no RAW; drive mode = continuous; autofocus = AI SERVO; ISO = Auto, max3200; file numbering = Auto reset; image auto rotate = Off; time/date = UTC.</li>
		</ol>
		</li>
		<li>Action camera
		<ol>
			<li>Set to video mode or continuous shooting mode at the highest resolution and frame rate possible. 
			NOTE: The action camera can also be used in continuous mode as long as the frame rate is 2 images per second or greater.</li>
			<li>To reproduce the protocol in this example (see Table of Materials), use a waterproof action camera with the following settings: Video resolution = 4K (4:3 aspect ratio); frame rate = 30 fps. 
			NOTE: For action cameras, it may be easier to attach the line from the spool to the swimmer rather than to the camera. In this example, the line was attached to the swimmer&#39;s wrist via a small lanyard.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Spool rig (Figure 1A)
	<ol>
		<li>Ensure that the spool is of the appropriate size to hold the length of line needed for the survey site radius. 
		NOTE: The circumference of the spool controls the spacing of the spiral swim lines, and the length of the line determines the sample area. In this example, an ~8 inch (~20 cm) diameter spool was used for ~50 inch (~1.3 m) spacing of swim lines. See 9 for details.</li>
		<li>Select a spool rig with a flanged edge (for smoothly guiding the line on and off the spool) and attachment points for a handle and pole (to control height from substrate). Ensure that the spool rig is inherently negatively buoyant or made so with the addition of weights. 
		NOTE: In this example, polyvinyl chloride (PVC) pipes for the handle and pole were used, and the spool was 3D printed in polylactic acid plastic. However, the spool can be as simple as a large PVC pipe or any other round object with the desired diameter.
		<ol>
			<li>For frequent use and/or challenging field conditions, select a spool made of a more durable&#160;material such as aluminum.</li>
			<li>Make sure that the spool does not rotate on the pole or spin when in use.</li>
		</ol>
		</li>
		<li>Fix the line to the spool at one end and to a detachable clip at the other for connecting to the camera. 
		NOTE: The length of the line defines the radius of the site. Here, 6 m of line was used for sites of 12 m in diameter.</li>
	</ol>
	</li>
	<li>Calibration tiles
	<ol>
		<li>Although specialized calibration tiles are not necessary, ensure that negatively buoyant, recognizable objects of known size are included in the model for scale. Consider surge and current conditions to ensure that suitable materials are used, so that tiles remain stationary during photo collection. 
		NOTE: Here, scale marker templates available as part of certain software programs were printed on waterproof paper, which was attached to 1-inch-thick PVC tiles.</li>
		<li>Divers will need a means to measure the depth of the tile. In our example, we use an electronic depth gauge (see Table of Materials).</li>
	</ol>
	</li>
	<li>Color correction
	<ol>
		<li>Set white balance on the camera to custom. Take a photo of an 18% grey card or white dive slate underwater before the start of every SfM survey. Do this every time a new site is started. 
		NOTE: The photo will allow for color correction and will also help to separate the downloaded images from different sites when conducting multiple surveys on the same day.</li>
	</ol>
	</li>
</ol>
2. Detailed methods
<ol>
	<li>Site selection
	<ol>
		<li>Select a site that has enough room to swim the entirety of the spiral pattern (~113 m2 in this example). In addition to the area being surveyed, incorporate a small buffer area to ensure that the entire survey area is sufficiently photographed to yield high-quality data.</li>
		<li>Consider the ability and equipment of the two-person team. Shallow sites (&#60; ~2 m) can be surveyed on snorkel, whereas deeper sites may require SCUBA.</li>
	</ol>
	</li>
	<li>If planning to repeatedly survey the site regularly, mark the center point, where the spool rig will be placed, with a tag or a permanent structure (e.g., rebar or cinder block). At the very least, take a global positioning system coordinate so that the site can be relocated with assistance from a printout of the orthomosaic. 
	NOTE: Permanent underwater structures typically require a permit.</li>
	<li>Prepare the site.
	<ol>
		<li>Set the spool in the middle of the site.</li>
		<li>Set out calibration tiles and record their depths. Place calibration tiles face-up, ~2 m away from the center. 
		NOTE: In this example, 3 calibration tiles were placed in a triangle around the center of the site. Calibration tiles should be appropriately weighted and positioned to ensure minimal movement during the collection of the photos.</li>
	</ol>
	</li>
	<li>Instruct the swimmer to swim with the camera while the assistant tends the spool.
	<ol>
		<li>The assistant sets the pole and the attached spool upright in the center of the selected site and holds the spool rig upright and stationary.</li>
		<li>Ensure that the swimmer attaches the side of the camera closest to the spool to the line and holds the camera facing straight down ~1 m from the benthos. 
		NOTE: If the swimmer must tilt the camera, try to make sure that it is tilted slightly forward rather than backwards to avoid collecting images in the swimmer&#39;s shadow. Tilting the camera slightly forward for both the outward spiral and the return spiral may also capture better angles of the benthos and produce better models, especially when there are overhangs and holes.</li>
		<li>Once the camera is properly positioned, the swimmer begins taking continuous images of the benthos while swimming forward and maintaining tension on the line.</li>
		<li>Ensure that the swimmer continues to swim in a spiral at a consistent speed while taking photographs until the line is completely unwound from the spool. 
		NOTE: The swimmer should try to stay a constant distance of ~ 1 m above the benthos and swim the spiral at a moderate pace to ensure sufficient overlap between images. When in doubt, slower is better.</li>
		<li>In highly rugose environments (e.g., coral reefs), include a third worker (second assistant) who can prevent line entanglement by hovering above the center of the line and gently lifting it over obstacles.</li>
		<li>When the line is completely unspooled, the swimmer reverses directions, reattaching the camera if necessary, and swims the camera in the opposite direction to begin re-winding the line back onto the spool while taking pictures. NOTE: Swimming the reverse spiral is not absolutely necessary, but will typically produce better models.</li>
		<li>If a single spiral method is desirable to save time, then the swimmer would detach the line from the camera and skip to step 2.4.12 while the assistant winds the line and removes the spool rig from the site.</li>
		<li>As soon as the swimmer begins to swim in the opposite direction the assistant rotates the spool to wind the line in &#189; of a turn (180&#176;) against the new swimming direction. This &#189; turn ensures that the swimmer&#8217;s return path is offset from the original path to yield greater photo coverage of the site.</li>
		<li>Ensure that the swimmer continues to take pictures and swim the reverse spiral until the line is almost completely rewound around the spool.</li>
		<li>When the swimmer&#8217;s and assistant&#8217;s spacing prevents further progress, the swimmer will then stop taking pictures to detach the camera from the line and allow the assistant to remove the spool rig from the center of the site.</li>
		<li>Once the spool is removed from the site, the swimmer images the center of the site by holding the camera facing straight down and moving the camera in a small spiral pattern over the center of the site.</li>
	</ol>
	</li>
</ol>
3. Clean up the site.
<ol>
	<li>Pick up calibration tiles and any other equipment before departing the site. 
	NOTE: Never leave trash or equipment at a site. Always leave a site cleaner than you found it.</li>
</ol>

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The authors have no competing financial interests or other conflicts of interest.

This study demonstrates that both the DSLR camera and the action camera produce models with better than 0.5 mm/pixel resolution in less than 10 h of processing time on a standard desktop computer. The major tradeoff between the DSLR and action camera, aside from cost, is finer resolution versus faster processing time, respectively. However, the reported processing times only include the computational processing. Thus, although the computational time is less for the action camera, there is a significant amount of time (10-20 min) invested in image extraction from the videos that is not required with the DSLR. An alternative is to use the action camera in continuous shooting mode to avoid image extraction. Continuous shooting mode was not used in this example, as the action camera can only shoot at 2 fps, which requires a significantly slower swim-rate to collect enough images to build a complete model. In this regard, there is a tradeoff between longer time in the field using the continuous shooting mode versus longer time on the computer, extracting images, when using video mode.
Advantages of the action camera include affordability and ease of transport and operation underwater. The main advantage of the DSLR is that it produces higher resolution images; hence, DSLR cameras are recommended over action cameras when the former is not cost-prohibitive. The kinds of questions a study seeks to address will also be important in determining the method used. For instance, an action camera might be preferable in environments that are relatively homogenous (e.g., seagrass beds, dead coral/rubble habitats), or where only broad community metrics (such as abundance, diversity) are being assessed over large spatial scales. However, a DSLR camera might be deployed in cases where tracking fine-scale changes in individual organisms or substrates is of interest.
As this is a field method, the model outputs will depend on various environmental factors such as lighting, water clarity, surface conditions, amount of surge, and movement of fish or non-stationary benthic structures (e.g., sea grass). Although there are no absolute thresholds of when it is appropriate to use this method, slightly overcast days with high water clarity, calm surface conditions, and little surge typically produce the best models. Moreover, there is a limit to the minimum depth required for these methods. These methods do not work well under conditions where there is less than 0.5 m of water because of the low overlap between photos and fewer distinguishing features per photo. However, this does highlight another advantage of the action camera, i.e., they are smaller and thus are easier for use at shallower depths. Furthermore, a smaller diameter spool and higher frame rate (or wider-angle lens) can improve image overlap in very shallow conditions9.
Many other data types can be integrated with this approach. For example, orthomosaics have been used to show the spatial density of molecular data (e.g., genes and metabolites) on corals24 and humans25 using the open source software &#39;ili&#39;26. The same platform could also be used to map the spatial densities of animals, microorganisms, viruses, and/or chemicals in the environment. Other examples have used SfM for annotating benthic species spatially onto orthomosaics using geographic information system software10. Furthermore, the 3D models generated by SfM can be used to estimate habitat characteristics such as rugosity and fractal dimension. Indeed, the methods outlined here were recently used to derive a new geometric theory for habitat surfaces10. Finally, orthomosaics are being used as input surfaces for spatially explicit computational models, allowing for dynamical simulations to be overlaid on the model&#39;s 3D surface. Being able to easily generate large images and 3D representations of benthic habitats has allowed marine scientists to address hitherto unimagined questions3.
Overall, here is a detailed protocol for conducting underwater SfM photogrammetry with either DSLR cameras or more cost-effective action cameras. These methods can be used by scientists for a broad range of purposes, from extracting data about benthic ecosystems to developing 3D input surfaces for in silico simulations. However, these protocols can also be used by non-scientists as part of citizen science efforts to gather valuable information on patterns of biodiversity, habitat complexity, community structure, and other ecological metrics.

Ecosystem processes are naturally dynamic and can be difficult to quantify. The past decade has seen a surge in new technologies for capturing ecosystems and their dynamics in a range of scales from 3D laser scanning of individual ecosystem features to satellite remote sensing of large areas1,2,3. In benthic habitats, structure is intimately connected with ecosystem function8, making tools that simultaneously allow for monitoring geometry and community structure especially valuable for understanding ecological dynamics. However, many modern approaches cannot be used in aquatic systems due to the physical properties of water (e.g., refraction, distortion, turbidity). Techniques, such as LiDAR (Light Detection and Ranging) and some aerial survey methods, may be appropriate on large spatial scales, but cannot acquire the resolution needed to assess fine scale changes in benthic habitats. Structure-from-Motion (SfM) photogrammetry methods have recently been adapted to produce large-scale, high-resolution orthomosaics and 3D surface models of underwater habitats4,5,6,7.
SfM photogrammetry is a relatively low-cost, simple, non-invasive, and repeatable method that allows for the generation of large-scale, high-resolution records of the benthic environment in aquatic ecosystems9. SfM uses a sequence of 2D images to generate 3D model reconstructions. The models generated from SfM can be used to collect data on the structural complexity (e.g., rugosity, dimensionality)4,5,10,11,12 and community structure (e.g., species composition, population demography)13,14,15 of benthic ecosystems. Furthermore, as this method is relatively inexpensive, quick, and repeatable, it can be used by both scientists and non-scientists to gather valuable, objective information on these ecosystems. Therefore, this method is a viable technique for use in citizen science projects where standardization of sampling effort, minimization of bias, engagement of participants, and ease of training are vital to the quality of data and overall success16,17.
This article provides a detailed protocol for conducting underwater SfM surveys. Simultaneously, the use of a DSLR camera has been compared with that of a more cost-effective &#39;action camera&#39;, and the relative advantages and disadvantages of each are outlined. The overall objective is to familiarize scientists and non-scientists with benthic SfM survey methods as rapidly as possible by providing a simple, commonly used protocol, in turn, promoting the use of this method more widely. &#160;For examples of studies that have applied variations of this method to study underwater ecological communities, see Burns et al. (2015)4, Storlazzi et al. (2016)18, Ventura et al (2016 and 2018)19,20, Edwards et al. (2017)14, George et al. (2018)21, Anelli et al. (2019)22, and Torres-Pulliza et al. (2020)10.
The method described here requires a two-person snorkel or SCUBA team. After the survey site is selected, a spool of line (Figure 1A) is placed at the center of the site, and calibration tiles (Figure 1B) are distributed ~2 m from the center. One person (the swimmer) swims with the camera and captures images of the site, while the second person (the assistant) tends the spool in the center of the plot (Figure 1C). First, the swimmer connects the camera to the spool via the line and then begins to take continuous pictures of the benthos while swimming face-down and forward to unwind the line off the spool. The swimmer should maintain a vertical distance of ~1 m above the substrate at all times, adjusting their position to match that of the topography as they swim. Importantly, the line connecting the spool and camera should remain taut at all times to create even spacing in the spiral as the swimmer surveys the plot. The assistant maintains the spool in a stable, upright position and ensures that the spool does not rotate, and that the line does not become tangled.
Once the line has been completely unwound, the swimmer stops, turns, and swims in the opposite direction to recoil the line around the spool. As the swimmer switches directions, the assistant turns the spool to wind the line in, exactly 180&#176; to prevent exact overlap of the outgoing path. Once the swimmer is as close to the center as possible, the camera is detached from the line, and the assistant takes the spool and line and swims away from the central portion of the site. The swimmer then finishes imaging the center of the plot by moving the camera in a small spiral over the center. While there are several ways to image an area effectively, the spool-and-line method described here is robust in even non-ideal environmental conditions where choppy surface waters, swell, or low visibility might otherwise impede data collection. In these scenarios, this method keeps snorkelers/divers attached and ensures high overlap of images by keeping the swimmer on a controlled path.

<table><tbody><tr><td>Action camera (GoPro Hero7 Black)</td><td>GoPro</td><td></td><td>Could be any waterproof action camera</td></tr><tr><td>Adobe Lightroom</td><td>Adobe</td><td></td><td>Color correction</td></tr><tr><td>Calibration tiles ( flat PVC board cut to size for Agisoft targets. Attach a dive weight underneath if expecting waves)</td><td></td><td></td><td>Any negatively buoyant object of known size and color. We recommend using the scale marker templates available from Agisoft Metashape software (v.1.6.0).</td></tr><tr><td>DSLR camera (Canon EOS Rebel SL3 )</td><td>Canon</td><td>3453C002AA</td><td>Could be any DSLR camera in a underwater housing</td></tr><tr><td>Line (plastic clothes line filament)</td><td></td><td></td><td>Any negatively buoyant line that is strong enough to withstand field use</td></tr><tr><td>Micro SDXC memory card (for GoPro)</td><td></td><td></td><td></td></tr><tr><td>Oceanic Veo 2.0</td><td>Oceanic</td><td></td><td>Digital depth gauge</td></tr><tr><td>SDXC memory card (for DSLR)</td><td></td><td></td><td>Any SDXC memory card should work, so long as there is enough space to hold all the pictures necessary to build the model&amp;nbsp;</td></tr><tr><td>Spool (2 inch-long section of 8 inch diameter PVC pipe which was attached to a 3 feet section of 1 inch PVC pipe to form the stem</td><td></td><td></td><td>Any negatively buoyant, round object of the desired diameter</td></tr><tr><td>Underwater camera housing for DSLR (Ikelite 200DLM/C Underwater TTL Housing)</td><td>Ikelite</td><td>6970.09</td><td>Should be the specific water housing for the DSLR make and model</td></tr><tr><td>Windows 10 desktop computer with an Intel i9-9900K 8-core CPU, two Nvidia GeForceRTX 2070 SUPER GPUs, and 128 GB of RAM.&amp;nbsp;</td><td></td><td></td><td>Processing&amp;nbsp;</td></tr></tbody></table>

a field primer for monitoring benthic ecosystems using structure-from-motion photogrammetry

Structure-from-motion (SfM) photogrammetry is a technique used to generate three-dimensional (3D) reconstructions from a sequence of two-dimensional (2D) images. SfM methods are becoming increasingly popular as a noninvasive way to monitor many systems, including anthropogenic and natural landscapes, geologic structures, and both terrestrial and aquatic ecosystems. Here, a detailed protocol is provided for collecting SfM imagery to generate 3D models of benthic habitats. Additionally, the cost, time efficiency, and output quality of employing a Digital Single Lens Reflex (DSLR) camera versus a less expensive action camera have been compared. A tradeoff between computational time and resolution was observed, with the DSLR camera producing models with more than twice the resolution, but taking approximately 1.4-times longer to produce than the action camera. This primer aims to provide a thorough description of the steps necessary to collect SfM data in benthic habitats for those who are unfamiliar with the technique as well as for those already using similar methods.

In this example, Reef Site 2&#173;&#173;_7 located on Patch Reef 13 in K&#257;ne&#699;ohe Bay, O&#699;ahu, Hawai&#699;i, was imaged, and 3,125 JPEG photos from the DSLR and 3,125 JPEG frame captures from the action camera video (Table 1) were used as input to create the orthomosaics and 3D models. The general workflow consisted of 5 stages: 1) alignment of photos to generate the sparse point cloud, 2) scaling the sparse point cloud and optimizing cameras, 3) building the dense point cloud (depth maps were also generated during this stage), 4) building the digital elevation model (DEM) and orthomosaic, and 5) generating the 3D model and texture. Note that stages 4 and 5 do not necessarily need to be done in that order, but they must be performed after processing the dense point cloud and depth maps. Georeferencing the models should occur prior to generating the orthomosaic and DEM. The settings used for these stages and processing details are outlined in Table 2 and Table of Materials, respectively.
For more detailed methods of how to generate 3D models and orthomosaics see the Supplementary Material and Suka et al.23. Processing time was shorter for the action camera-derived model for every step including sparse point cloud generation, dense point cloud generation, mesh model rendering, and textured model rendering. This led to a significantly faster overall processing time for the action camera model (6 h 39 min) than the DSLR model (9 h 14 min). The exact time for model processing will vary with computational power and specific hardware configurations.
The model generated using images from the DSLR camera contained 2,848,358 sparse cloud points and 787,450,347 dense cloud points while the model generated from the action camera images contained only 2,630,543 sparse cloud points and 225,835,648 dense cloud points. This led to the DSLR models having more than 2x the resolution than the action camera models with orthomosaics resolutions of 0.442 and 0.208 mm/pixel for the DSLR- and action camera-derived models, respectively (Table 1). Despite the better resolution of the DSLR model relative to the action camera model, both methods were able to produce high-quality models with little difference in visual representation when the ~113 m2 reef area was represented as a 20 cm2 digital elevation model (Figure 2 top panels) or 2D orthomosaic projection (Figure 2 middle panels).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61815/61815fig01.jpg" /> 
Figure 1: Structure-from-Motion photogrammetry. (A) Example of a spool rig for controlling swimmer distance with an attached handle and pole for precise positioning and handling. (B) Calibration tiles. (C) A schematic of the swim path with relative positions of the swimmer (green) and the assistant (orange). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61815/61815fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61815/61815fig02.jpg" /> 
Figure 2: Visual comparison of digital elevation models and orthomosaics. Digital elevation models (top) and orthomosaics (middle) constructed from DSLR (left) and action camera (right) images. The bottom panel is a zoom of the areas in the white boxes in the orthomosaics. The heatmap scales in the top panel represent distance from the surface of the water in meters (m). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61815/61815fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Canon EOS Rebel SL3</td>
			<td>GoPro Hero 7</td>
		</tr>
		<tr>
			<td>Cost</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Camera</td>
			<td>~$600.00</td>
			<td>~$220.00</td>
		</tr>
		<tr>
			<td>Underwater housing</td>
			<td>~$1,700.00</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>Total Cost</td>
			<td>~$2,300.00</td>
			<td>~$220.00</td>
		</tr>
		<tr>
			<td>Photos</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Photo file format</td>
			<td>jpeg</td>
			<td>jpeg</td>
		</tr>
		<tr>
			<td>Photo resolution</td>
			<td>24 Megapixels</td>
			<td>12 Megapixels (from 4K video)</td>
		</tr>
		<tr>
			<td>Aligned photos / total photos</td>
			<td>3125 / 3125</td>
			<td>3125 / 3125</td>
		</tr>
		<tr>
			<td>Photogrammetry metrics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sparse cloud points</td>
			<td>2,848,358</td>
			<td>2,630,543</td>
		</tr>
		<tr>
			<td>Dense cloud points</td>
			<td>787,450,347</td>
			<td>225,835,648</td>
		</tr>
		<tr>
			<td>Faces (3D model)</td>
			<td>11,919,451</td>
			<td>3,834,651</td>
		</tr>
		<tr>
			<td>Digital elevation model (DEM) resolution</td>
			<td>0.831 mm/pixel</td>
			<td>1.77 mm/pixel</td>
		</tr>
		<tr>
			<td>Orthomosaic resolution</td>
			<td>0.208 mm/pixel</td>
			<td>0.442 mm/pixel</td>
		</tr>
		<tr>
			<td>Processing times</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sparse cloud generation</td>
			<td>1 h 23 min</td>
			<td>1 h 27 min</td>
		</tr>
		<tr>
			<td>Dense cloud generation</td>
			<td>4 h</td>
			<td>3 h 11 min</td>
		</tr>
		<tr>
			<td>Mesh model rendering</td>
			<td>3 h 32 min</td>
			<td>1 h 49 min</td>
		</tr>
		<tr>
			<td>Texture rendering</td>
			<td>19 min</td>
			<td>12 min</td>
		</tr>
		<tr>
			<td>Total computer processing time</td>
			<td>9 h 14 min</td>
			<td>6 h 39 min</td>
		</tr>
	</tbody>
</table>
Table 1: Detailed information about setup cost, photos used to construct the models, photogrammetry metrics, and processing time. Processing was done using the same settings for both models. Note that processing time does not include time for various steps such as photo editing, extracting images from video, re-aligning photos, and editing and scaling the models.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Canon EOS Rebel SL3</td>
			<td>GoPro Hero 7</td>
		</tr>
		<tr>
			<td>Images</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Average file size</td>
			<td>~ 8.3 MB</td>
			<td>~ 4.7 MB</td>
		</tr>
		<tr>
			<td>Photo acquisition</td>
			<td>Continuous mode</td>
			<td>Extracted from 4K video</td>
		</tr>
		<tr>
			<td>Color correction</td>
			<td>Manual</td>
			<td>Manual</td>
		</tr>
		<tr>
			<td>Lens correction</td>
			<td>No</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>Photogrammetry Process Settings</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sparse cloud generation</td>
			<td>Accuracy: High</td>
			<td>Accuracy: High</td>
		</tr>
		<tr>
			<td></td>
			<td>Key Point: 40,000</td>
			<td>Key Point: 40,000</td>
		</tr>
		<tr>
			<td></td>
			<td>Tie Point: 4,000</td>
			<td>Tie Point: 4,000</td>
		</tr>
		<tr>
			<td></td>
			<td>Generic Preselection: Yes</td>
			<td>Generic Preselection: Yes</td>
		</tr>
		<tr>
			<td>Dense cloud generation</td>
			<td>Medium Quality</td>
			<td>Medium Quality</td>
		</tr>
		<tr>
			<td>3D mesh model generation</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Source data:</td>
			<td>Depth Maps</td>
			<td>Depth Maps</td>
		</tr>
		<tr>
			<td>Quality:</td>
			<td>Medium</td>
			<td>Medium</td>
		</tr>
		<tr>
			<td>Face count:</td>
			<td>Low</td>
			<td>Low</td>
		</tr>
		<tr>
			<td>Interpolation:</td>
			<td>Enabled</td>
			<td>Enabled</td>
		</tr>
		<tr>
			<td>Calculate vertex colors:</td>
			<td>Yes</td>
			<td>Yes</td>
		</tr>
		<tr>
			<td>3D texture generation</td>
			<td rowspan="1"></td>
			<td rowspan="1"></td>
		</tr>
		<tr>
			<td>Texture type:</td>
			<td>Diffuse Map</td>
			<td>Diffuse Map</td>
		</tr>
		<tr>
			<td>Source data:&#160;</td>
			<td>Images</td>
			<td>Images</td>
		</tr>
		<tr>
			<td>Mapping mode:</td>
			<td>Generic</td>
			<td>Generic</td>
		</tr>
		<tr>
			<td>Blending mode:</td>
			<td>Mosaic</td>
			<td>Mosaic</td>
		</tr>
		<tr>
			<td>Texture size/count:</td>
			<td>4096 / 1</td>
			<td>4096 / 1</td>
		</tr>
		<tr>
			<td>Digital elevation model (DEM)</td>
			<td>From Dense cloud</td>
			<td>From Dense Cloud</td>
		</tr>
		<tr>
			<td>Orthomosaic</td>
			<td>From DEM</td>
			<td>From DEM</td>
		</tr>
	</tbody>
</table>
Table 2: Detailed information on collected images and photogrammetric processing. Processing was done using the same settings for both models.
Supplementary Material.&#160;&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61815/JoVE_SI_Roach.docx" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry at JoVE.com

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

We provide a detailed protocol for conducting underwater structure-from-motion photogrammetry surveys to generate 3D models and orthomosaics.

Structure-from-motion (SfM) photogrammetry is a technique used to generate three-dimensional (3D) reconstructions from a sequence of two-dimensional (2D) ...

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3.6K Views.  University of Hawai'i Manoa. We bieden een gedetailleerd protocol voor het uitvoeren van fotogrammetrie-onderzoeken van de onderwaterstructuur uit beweging om 3D-modellen en orthomosaics te genereren.

Video: A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

The Benthic Exchange of O2, N2 and Dissolved Nutrients Using Small Core Incubations

The measurement of sediment-water exchange of gases and solutes in aquatic sediments provides data valuable for understanding the role of sediments in nutrient and gas cycles. After cores with intact sediment-water interfaces are collected, they are submerged in incubation tanks and kept under aerobic conditions at in situ temperatures. To initiate a time course of overlying water chemistry, cores are sealed without bubbles using a top cap with a suspended stirrer. Time courses of 4-7 sample points are used to determine the rate of sediment water exchange. Artificial illumination simulates day-time conditions for shallow photosynthetic sediments, and in conjunction with dark incubations can provide net exchanges on a daily basis. The net measurement of N2 is made possible by sampling a time course of dissolved gas concentrations, with high precision mass spectrometric analysis of N2:Ar ratios providing a means to measure N2 concentrations. We have successfully applied this approach to lakes, reservoirs, estuaries, wetlands and storm water ponds, and with care, this approach provides valuable information on biogeochemical balances in aquatic ecosystems.

The Benthic Exchange of O2, N2 and Dissolved Nutrients Using Small Core Incubations

Here, we present small core incubations for the measurement of sediment-water gas and solute exchange. These will provide reliable measurements of sediment-water exchange that assess the role of sediment in influencing biological and biogeochemical processes in aquatic ecosystems.

The measurement of sediment-water exchange of gases and solutes in aquatic sediments provides data valuable for understanding the role of sediments in ...

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropod populations in rice. When used in combination with an enclosure, application of this sampling device provides absolute estimates of the populations of arthropods as numbers per standardized sampling area. The sampling efficiency depends critically on the sampling duration. In a mature rice crop, a two-minute sampling in an enclosure of 0.13 m2 yields more than 90% of the arthropod population. The device also allows sampling of arthropods dwelling on the water surface or the soil in rice paddies, but it is not suitable for sampling fast flying insects, such as predatory Odonata or larger hymenopterous parasitoids. The modified blower-vac is simple to construct, and cheaper and easier to handle than traditional suction sampling devices, such as D-vac. The low cost makes the modified blower-vac also accessible to researchers in developing countries.

Assessment of arthropod abundance in crops is critical for investigating population dynamics and species interactions. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropods in rice.

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the ...

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions

We describe techniques for approximating seed bank dynamics over time using Helianthus annuus as an example study species. Strips of permeable polyester fabric and glue can be folded and glued to construct a strip of compartments that house seeds and identifying information, while allowing contact with soil leachate, water, microorganisms, and ambient temperature. Strips may be constructed with a wide range of compartment numbers and sizes and allow the researcher to house a variety of genotypes within a single species, different species, or seeds that have experienced different treatments. As opposed to individual seed packets, strips are more easily retrieved as a unit. While replicate packets can be included within a strip, different strips can act as blocks or can be retrieved at different times for observation of seed behavior over time. We used a high temperature glue gun to delineate compartments and sealed the strips once the seed and tags identifying block and removal times were inserted. The seed strips were then buried in the field at the desired depth, with the location marked for later removal. Burrowing animal predators were effectively excluded by use of a covering of metal mesh hardware cloth on the soil surface. After the selected time interval for burial, strips were dug up and seeds were assessed for germination, dormancy and mortality. While clearly dead seeds can often be distinguished from ungerminated living ones by eye, dormant seeds were conclusively identified using a standard Tetrazolium chloride colorimetric test for seed viability.

Here we present a protocol for assessing seed survivorship, germination and dormancy under field conditions using buried, labeled seed strips and tetrazolium chloride (TZ) viability testing.

We describe techniques for approximating seed bank dynamics over time using Helianthus annuus as an example study species. Strips of permeable polyester ...

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

Plant-pollinator interactions have been studied for approximately one hundred years. During that time, many field methods have been developed to clarify the pollination effectiveness of each pollinator for visited flowers. Pollinator observations have been one of the most common methods to identify pollinators, and bagging and cage experiments have been conducted to show the effectiveness of specific pollinators. In a previous study of Lycoris sanguinea var. sanguinea, its effective pollinators, the visitation frequencies of each floral visitor, and its reproductive strategies were not identified. This study reports the observation that small bees visited flowers that were partially opened (breaking buds). To the best of our knowledge, this phenomenon has not been reported previously. Further, this study investigates the hypothesis that small bees can pollinate at that flowering stage. This study demonstrates the basic methods of field experiments in pollination ecology using L. sanguinea var. sanguinea. Pollinator observations and digital video showed the visitation frequencies of each floral visitor. Bagging and cage experiments revealed that these flowers could be pollinated fully and that breaking-bud pollination could be important for the pollination of this plant species. The advantages and disadvantages of each method are discussed, and recent developments, including laboratory experiments, are described.

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

To reveal the pollinator effectiveness of a given plant species, multiple methods of field experiments have been developed. This study demonstrates the basic methods of field experiments for pollination ecology using the case study of Lycoris sanguinea var. sanguinea and the novel pollination mechanism, breaking-bud pollination.

Plant-pollinator interactions have been studied for approximately one hundred years. During that time, many field methods have been developed to clarify ...

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Life tables provide a means of measuring the schedules of birth and death from populations over time. They also can be used to quantify the sources and rates of mortality in populations, which has a variety of applications in ecology, including agricultural ecosystems. Horizontal, or cohort-based, life tables provide for the most direct and accurate method of quantifying vital population rates because they follow a group of individuals in a population from birth to death. Here, protocols are presented for conducting and analyzing cohort-based life tables in the field that takes advantage of the sessile nature of the immature life stages of a global insect pest, Bemisia tabaci. Individual insects are located on the underside of cotton leaves and are marked by drawing a small circle around the insect with a non-toxic pen. This insect can then be observed repeatedly over time with the aid of hand lenses to measure development from one stage to the next and to identify stage-specific causes of death associated with natural and introduced mortality forces. Analyses explain how to correctly measure multiple mortality forces that act contemporaneously within each stage and how to use such data to provide meaningful population dynamic metrics. The method does not directly account for adult survival and reproduction, which limits inference to dynamics of immature stages. An example is presented that focused on measuring the impact of bottom-up (plant quality) and top-down (natural enemies) effects on the mortality dynamics of B. tabaci in the cotton system.

Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

Life tables allow quantification of the sources and rates of mortality in insect populations and contribute to understanding, predicting and manipulating population dynamics in agroecosystems. Methods for conducting and analyzing cohort-based life tables in the field for an insect with sessile immature life stages are presented.

Life tables provide a means of measuring the schedules of birth and death from populations over time. They also can be used to quantify the sources and ...

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

The use of video camera systems in ecological studies of fish continues to gain traction as a viable, non-extractive method of measuring fish lengths and estimating fish abundance. We developed and implemented a rotating stereo-video camera tool that covers a full 360 degrees of sampling, which maximizes sampling effort compared to stationary camera tools. A variety of studies have detailed the ability of static, stereo-camera systems to obtain highly accurate and precise measurements of fish; the focus here was on the development of methodological approaches to quantify fish density using rotating camera systems. The first approach was to develop a modification of the metric MaxN, which typically is a conservative count of the minimum number of fish observed on a given camera survey. We redefine MaxN to be the maximum number of fish observed in any given rotation of the camera system. When precautions are taken to avoid double counting, this method for MaxN may more accurately reflect true abundance than that obtained from a fixed camera. Secondly, because stereo-video allows fish to be mapped in three-dimensional space, precise estimates of the distance-from-camera can be obtained for each fish. By using the 95% percentile of the observed distance from camera to establish species-specific areas surveyed, we account for differences in detectability among species while avoiding diluting density estimates by using the maximum distance a species was observed. Accounting for this range of detectability is critical to accurately estimate fish abundances. This methodology will facilitate the integration of rotating stereo-video tools in both applied science and management contexts.

We describe a new method for counting fishes, and estimating relative abundance (MaxN) and fish density using rotating stereo-video camera systems. We also demonstrate how to use distance from camera (Z distance) to estimate species-specific detectability.

The use of video camera systems in ecological studies of fish continues to gain traction as a viable, non-extractive method of measuring fish lengths and ...

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the approach more practical than in the past. Image-based surveys allow for initial samples to be revisited and are non-invasive compared to traditional survey methods that typically involve nets or dredges. Protocols for image-based surveys can vary greatly but should be driven by target species behavior and survey objectives. To demonstrate this, we describe our most recent methods for an Atlantic sea scallop (Placopecten magellanicus) drop camera survey to provide a procedural example and representative results. The procedure is divided into three critical steps that include survey design, data collection, and data products. The influence of scallop behavior and the survey goal of providing an independent assessment of the U.S. sea scallop resource on the survey procedure are then discussed in the context of generalizing the method. Overall, the broad applicability and flexibility of the University of Massachusetts Dartmouth School for Marine Science and Technology (SMAST) drop camera survey demonstrates the method could be generalized and applied to a variety of sessile invertebrates or habitat focused research.

Image based surveying is an increasingly practical, non-invasive method to sample the marine environment. We present the protocol of a drop camera survey that estimates the abundance and distribution of the Atlantic sea scallop (Placopecten magellanicus). We discuss how this protocol can be generalized for application to other benthic macroinvertebrates.

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the ...

Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ecosystems is required to assess rangeland health, to gauge habitat suitability for wildlife and domestic livestock, to combat invasive weeds, and to elucidate temporal environmental changes. Although rangeland ecosystems cover vast areas, traditional monitoring techniques are often time-consuming and cost-inefficient, subject to high observer bias, and often lack adequate spatial information. Image-based vegetation monitoring is faster, produces permanent records (i.e., images), may result in reduced observer bias, and inherently includes adequate spatial information. Spatially balanced sampling designs are beneficial in monitoring natural resources. A protocol is presented for implementing a spatially balanced sampling design known as balanced acceptance sampling (BAS), with imagery acquired from ground-level cameras and unmanned aerial systems (UAS). A route optimization algorithm is used in addition to solve the &#8216;travelling salesperson problem&#8217; (TSP) to increase time and cost efficiency. While UAS images can be acquired 2&#8211;3x faster than handheld images, both types of images are similar to each other in terms of accuracy and precision. Lastly, the pros and cons of each method are discussed and examples of potential applications for these methods in other ecosystems are provided.

The protocol presented in this paper utilizes route optimization, balanced acceptance sampling, and ground-level and unmanned aircraft system (UAS) imagery to efficiently monitor vegetation in rangeland ecosystems. Results from images obtained from ground-level and UAS methods are compared.

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for ...

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Body size is an important functional trait that can be used as a bioindicator to assess the impacts of perturbations in natural communities. Community size structure responds to biotic and abiotic gradients, including anthropogenic perturbations across taxa and ecosystems. However, the manual measurement of small-bodied organisms such as benthic macroinvertebrates (e.g., &gt;500 &#181;m to a few centimeters long) is time-consuming. To expedite the estimation of community size structure, here, we developed a protocol to semi-automatically measure the individual body size of preserved river macroinvertebrates, which are one of the most commonly used bioindicators for assessing the ecological status of freshwater ecosystems. This protocol is adapted from an existing methodology developed to scan marine mesozooplankton with a scanning system designed for water samples. The protocol consists of three main steps: (1) scanning subsamples (fine and coarse sample size fractions) of river macroinvertebrates and processing the digitized images to individualize each detected object in each image; (2) creating, evaluating, and validating a learning set through artificial intelligence to semi-automatically separate the individual images of macroinvertebrates from detritus and artifacts in the scanned samples; and (3) depicting the size structure of the macroinvertebrate communities. In addition to the protocol, this work includes the calibration results and enumerates several challenges and recommendations to adapt the procedure to macroinvertebrate samples and to consider for further improvements. Overall, the results support the use of the presented scanning system for the automatic body size measurement of river macroinvertebrates and suggest that the depiction of their size spectrum is a valuable tool for the rapid bioassessment of freshwater ecosystems.

The article is based on the creation of an adapted protocol to scan, detect, sort, and identify digitized objects corresponding to benthic river macroinvertebrates using a semi-automatic imaging procedure. This procedure allows the acquisition of the individual size distributions and size metrics of a macroinvertebrate community in about 1 h.

Body size is an important functional trait that can be used as a bioindicator to assess the impacts of perturbations in natural communities. Community ...

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the status, trends, and dynamics of organisms living in subtidal habitats. By creating photo-realistic digital twins for ex situ analyses, these approaches allow small field teams to collect substantially more data than was previously possible. Here we present a four-step, large-area imaging survey pipeline and analysis methodology, including image collection, model construction, ecological analysis, and data curation, that has been developed and refined through experimentation over the past decade. Each step described has a consistent focus on the unique value of the original source imagery. While types of data extracted from large-area image surveys are vast, we include here workflows to extract ecological data for structural complexity, community composition, and demographic analyses valuable for monitoring and hypothesis-driven efforts. We additionally include recommendations for metadata standards, which complement the collection of large-area imaging data and support archival efforts facilitating transparency and collaboration between research groups.

This protocol covers a four-step large-area imaging survey methodology used to extract metrics of structural complexity, community composition, and population demographics for coral reef communities. The quality of imagery collected and integrated access to the source imagery are prioritized within each step of the protocol.

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the ...

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

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