Monday, December 4, 2017

BioBase Paper Published: Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar.

We're excited to see another publication demonstrating another novel use of BioBase EcoSound technology for Fisheries Science. For a complete list of pubs see hereContact us to get a copy of any of these publications

Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar

Jason D. Schooley
Oklahoma Department of Wildlife Conservation

Ben C. Neely
Kansas Department of Wildlife, Parks, and Tourism

Journal of Applied Icthyology 2017

The paddlefish (Polyodon spathula Walbaum, 1792) is a springtime migrant that requires discrete abiotic conditions such as water temperature, discharge, and substrate composition for successful spawning and recruitment. Although population declines have prevailed throughout much of the species range, Oklahoma paddlefish are abundant and support popular recreational snag fisheries – most notably in Grand Lake. This stock utilizes the Grand Lake’s two primary headwaters, the Neosho and Spring rivers, with only episodic recruitment success. However, relationships between suitable spawning habitat and water level have not been evaluated in this system. Using consumer-grade sonar equipment, this study identified and quantified hard river substrates (such as cobble and bedrock) and investigated proportional habitat availability at a variety of simulated river conditions. Sonar data were used to construct 49-m2 grids of depth and bottom hardness (H) ranging from 0.0 (soft) -0.5 (hard). Ground-truthing samples of bottom composition were collected with a grab sampler and by visual identification. Substrate types were pooled into two categories: soft substrates (H < 0.386) and spawning substrates (H ≥ 0.386) allowing for estimation of available spawning habitat in each river. Spawning habitat comprised 69% of total available habitat for the Neosho River (6.5 ha/km) and 58% for the Spring River (7.9 ha/km). Estimated spawning habitat was simulated over a range of river stages and predictive models were developed to estimate proportional spawning habitat availability (PHA). Although the Spring River contains more concentrated spawning habitat in closer proximity to Grand Lake, the Neosho River contains a greater quantity over nearly twice the distance to the first migration barrier, has a larger watershed, and demonstrates greater PHA at lower river stages. Model results were validated in context of known high and low recruitment years, where a greater frequency and duration of days with ≥90% PHA were observed in good recruitment years, particularly in the Neosho River. In total, results suggest the Neosho River has greater value for paddlefish reproduction than the Spring River. Research-informed harvest management will remain critical to the conservation of wild-recruiting stocks for continued recreational use in Oklahoma.

Average Neosho and Spring river substrate hardness index (H) for substrate classification groups across pooled methods (grab samples and visual samples). Cobble/Rock includes fine, medium, and coarse cobble pooled with bedrock. Substrates represented by H ≥ 0.386 were regarded as paddlefish spawning habitat. Sample size is noted at the base of each column and error bars indicate 95% confidence intervals
Schooley JD, Neely BC. Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar. J Appl Ichthyol. 2017;00:1–9.

Thursday, July 27, 2017

Guest Blog: Correlations between EcoSound Biovolume and Aquatic Plant Biomass

Andrew W. Howell and Dr. Robert J. Richardson

North Carolina State University; Dept. Crop and Soil Sciences

Why do we want to sample submersed vegetation biomass using sonar?

Invasive aquatic plants, such as non-native hydrilla (Hydrilla verticillata), negatively impact waterway systems in the southeastern United States and on a global scale. Often, these aquatic weed species impede recreational activities, power generation, and disrupt native ecological systems. Costs associated with aquatic weed management include expenses accompanied with monitoring, mapping, and implementing control measures. Prompt detection and accurate mapping of submersed aquatic vegetation (SAV) are critical components when formulating management decisions and practices. Therefore, SAV management protocols are often reliant upon the perceived extent of invasion. Traditional biomass sampling techniques have been widely utilized, but often require significant labor inputs, which limits repeatability, the scale of sampling, and the rapidness of processing. Advances in consumer available hydroacoustic technology (sonar) and data post-processing offer the opportunity to estimate SAV biomass at scale with reduced labor and economic requirements.

The objectives of this research were to document the use of an off-the-shelf consumer sonar/gps chartplotter to: 1) describe and characterize a relationship between hydroacoustic biovolume signature to measured hydrilla biomass; 2) develop algorithm for on-the-fly assessment of hydrilla biomass from interpolated biovolume records; 3) define seasonal hydrilla growth patterns at two NC piedmont reservoirs; and 4) create a visual representation of SAV development over time. From these objectives, the expected outcome was to describe a protocol for passive data collection while reducing the economic inputs associated with labor efforts involved in biomass sampling and post-processing evaluations. In our research, a Lowrance HDS-7 Gen2 was utilized to correlate biomass from monospecific stands of hydrilla within two different North Carolina piedmont reservoirs using BioBase 5.2 (now marketed as EcoSound -, cloud-based algorithm to aid in post-processing.

Methods for collecting biovolume and hydrilla biomass.

To determine the trend between biovolume and biomass, we sampled two NC piedmont reservoirs every two weeks for hydrilla biomass and acoustically-derived biovolume between mid-June and late-October 2015. The two NC piedmont waterbodies chosen for fixed sampling sites included Shearon Harris Reservoir (SH; Wake Co.; 35°38′0″N, 78°57′18″W) and Roanoke Rapids Lake (RR; Halifax Co.; 36°28'58.3"N, 77°43'38.7"W) (Figure 1). These locations were selected to simulate a range of ecological factors of bathymetric profile, littoral slope, water exchange frequency, and seasonal SAV growth rates. Based on previously conducted surveys by North Carolina State University, hydrilla has remained the dominant SAV species in both reservoirs for over a decade thus, providing an excellent scenario for testing biovolume and biomass correlations. 

Figure 1. Locations of two NC piedmont reservoirs sampled for hydrilla biomass and hydroacoustic scans; Shearon Harris (bottom center) and Roanoke Rapids (top right

      Hydroacoustic sampling and processing…
…took place using a HST-WSBL 200 khz transducer. Prior to each biomass sampling, a serpentine transect with 7.5 m spacing occurred throughout the plots to determine bottom area interpolation of SAV abundance at each labeled biomass sampling point. The *.sl2 sonar log representing each location was given a unique identifier so future correlation could be prepared. All logged data were saved by the chartplotter to an internal SD memory card for further analysis and upload to BioBase cloud-based software.

      Biomass plots and hydrilla collection… 

…was conducted from predetermined hydrilla plots (surface area of 0.53 ha-2 each) which were georeferenced at both study locations containing n=60 evenly spaced points per plot. For each of the sampling periods, four randomly selected points were selected per plot using a random number generator without replacement. These sample points were then used for all plots at both lake locations for that sampling period (ie. SH = 8 points biweekly; RR = 12 points biweekly). 

To sample biomass at each of the study sites, we used a modified version of the boat-based vertical rake method, proposed by Johnson and Newman (2011) for SAV biomass collection. We use the onboard global positioning system (GPS) from the echosounding unit to “hover” the boat over each designated random biomass point. The rake was then lowered near the boat-based transducer, spun twice, then returned back to the boat. Estimates of rake coverage and average stem length were recorded. If any non-hydrilla species were detected, those plants were separately bagged and analyzed. All hydrilla biomass collected were field washed, individually bagged for dry weight analysis, and placed in a cooler until reaching the lab where samples could then be oven dried and weighed. 

       Processing and data analysis…

…of all raw hydroacoustic data took place using the EcoSat algorithm to obtain biovolume estimates and depth of each sampling site. Using similar methods to Valley et al. (2015), representative plots at all sites were imported into ESRI’s ArcGIS 10.2.2 software for further post-processing and analysis. Using RStudio, the hydroacoustic and biomass dataset attributes were run through a correlation analysis and mapped to provide visual representation of temporal plant canopy development. Regression analysis and biomass prediction algorithms were also developed. Other factors such as percent area covered (PAC), depth, and seasonal biovolume development among plots at both SH and RR were also analyzed. Among all data, a false-positive limiting depth of 0.76 m-1 was assigned as the minimum depth used for correlation analysis. Removing data points from the shallow regions in this study did not impede overall analysis as plot layouts for biomass harvesting were designed to exceed 0.76 m-1 depths. 

So, what are the main findings from our study?

Throughout our experimental period, biomass was collected at 84 sampled points at SH and 71 sampled points at RR to represent a wide range of littoral features and seasonal variability among each test plot. The mean seasonal biovolume percentages and biomass accumulation varied less at RR than at the SH test sites. However, both locations had similar depth variability between individual test plots.

      Correlations between Biomass and Percent Biovolume

Biovolume percentages and field observed hydrilla biomass is shown in figures 5 and 6. At SH, we found that there was a strong positive trend which indicated biomass would increase with biovolume (Figure 2). Hydrilla growth at both test plots at SH contained low biovolume to biomass ratios, which may explain why there was a low association when biovolume reached ≥ 25% (Figure 2). Correlations from RR produced very strong agreement and represented high biovolume to biomass ratios well (Figure 3). Conversely, low biovolume estimates (ie. ≤ 25%) were not well represented (Figure 3). Independent correlations from both SH and RR study areas indicated strong, positive correlations. However, the pooled data provided the greatest explanation of association, as the combination of both lake data sets characterized a wide range of biovolume (0 to 100%), depth (0 to 4.74 m), and biomass ranges (0 – 446.1 g dry wt), that helped provide well spread data and the most accurate representation of seasonal hydrilla growth. The entire range of biovolume estimates were represented at both lake locations (Figure 4). Among every correlation, hydrilla biomass was often found at maximum in shallow depth locations. Overall, each biovolume to biomass relationship supports the initial hypothesis that as biovolume increases, SAV biomass should increase in a positive trend, although alterable, since bathymetry aids in defining biovolume estimations.  

Figure 2. Shearon Harris correlations among estimated biovolume percentages from BioBase 5.2 and observed hydrilla biomass (Spearman rank ρ: 0.51, P < 0.001
Figure 3. Roanoke Rapids correlations among estimated biovolume percentages from BioBase 5.2 and observed hydrilla biomass (Spearman rank ρ: 0.73, P < 0.001).

Figure 4. Pooled dataset correlations among estimated biovolume percentages from BioBase 5.2 and observed SAV biomass by sampling locations (Spearman rank ρ: 0.79, P < 0.001). From these observations, Shearon Harris best represents data points of low biovolume and low biomass where Roanoke Rapids more clearly depicts samples containing high biovolume and high biomass.

       Hydroacoustic biomass prediction algorithms

Study sites received predictive model parameters described by the ecological growth trends at all sampling locations since depth has been shown to limit the extent of vertical hydrilla growth in this study. Biomass prediction was strongest at SH than compared to either RR or the pooled dataset when cross-validated. Therefore, the most robust model for this study was determined using generalized additive models (GAMs) from the SH dataset, which also provided the highest range of prediction values of all models tested (Figure 5). 
      Seasonal hydrilla growth and temporal development…

…is a vital component of timely management applications and for studying aquatic weed composition levels within a water column (ie. biovolume). To illustrate seasonal plot biovolume accumulation, Figure 6 portrays an example of the ecological development pattern differences in biovolume over time at SH. We notice that hydrilla growth had the highest biovolume from early-September to mid-October 2015. While our growth pattern is not applicable to all growth patterns of SAV or waterbodies, lake and aquatic weed managers may find this visual appraisal extremely useful for describing seasonal growth or treatment effect patterns. 

Figure 6. Example of the temporal hydrilla growth pattern for one plot at Shearon Harris.

What management implications may be gleaned from this study? 

Our findings are consistent with those of Stent and Hanley (1985), Duarte and Kalff (1986), and Duarte and Kalff (1990) that biomass regression analysis is a site-specific procedure due to littoral slope, turbidity, water quality, and the presence of other aquatic plants. We have also shown, that even when using monospecific stands of hydrilla, there is variation of SAV biomass among discrete waterbodies. However, using GAMs to engage vigorous statistical procedures, the power of obtaining a more precise prediction model has potential for explaining environmental factors causing deviation. 

A few minor limitations involving the prediction of future hydrilla biomass were apparent in this study. These disadvantages were: 1) hydrilla biomass was highly variable as biovolume reached 100% water column occupancy; 2) once biovolume reached 100%, we were unable to predict future responses in our algorithms; 3) in areas where SAV height was at water surface, our boat was incapable of mapping those areas reliably without obstructing boat transects; and 4) we were not able to obtain biovolume estimates below 0.76 m-1 due to transducer noise. On a cautionary note, all biomass estimations occurring when biovolume is at 100% should be double checked with depth parameters to ensure model elements are not extrapolated beyond the extent of the dataset. Also, to overcome unrepresented areas containing SAV at either depths < 0.76 m-1, or areas containing 100% biovolume, spatial interpolation techniques such as kriging, IDW, bilinear interpolation, or nearest neighbor may be utilized to define those regions (Valley et al. 2005). 

Although some drawbacks were present with this research, the advantages of utilizing a consumer available echosounding unit for SAV biomass assessment far out compensated the obstacles formerly described. By exercising a third-party vendor (BioBase) for managing all data recorded by the echosounder, a major reduction in post-processing time was achieved. Furthermore, our methodology proved useful in both tracking and mapping temporal changes in biovolume and biomass accumulation over time. This not only offered a repeatable, non-destructive monitoring opportunity for ecological growth patterns, but also provided visual evidence for aquatic weed management applications. Aquatic plant managers may additionally want to employ the use of the algorithms in formulating proposals for herbicide treatments, grass carp stockings, or stakeholder reports.  Since this study focused solely on hydrilla, future studies may want to implicate validation of our models in similarly structured macrophytes for biomass estimation (eg. submersed Myriophyllum spp.).   

In conclusion, our study defines the parallel between biovolume and hydrilla biomass-- showing how technological advances used by aquatic ecologists conducting fixed point-intercept sampling protocols, may also passively record hydrilla biomass estimation using an over-the-counter echosounder. One key advantage of this echosounding unit was the ease of operation, availability, cost, and minimal training requirements. Likewise, managers could easily train employees to use this system in only a few minutes. Our findings should offer noteworthy economic efficiency with reduction to the degree of labor and time spent presenting spatiotemporal littoral zone dynamics as documented by Maceina et al. (1984). Following the results of this report, especially in lakewide survey scenarios, should offer improvements to traditional biomass sampling.

Duarte, C.M. 1987. Use of echosounder tracings to estimate the aboveground biomass of submerged plants in lakes. Can. J. Fish. Aquat. Sci. 44:732-735.
Duarte, C.M. and Kalff J. 1986. Littoral slope as a predictor of the maximum biomass of submerged macrophyte communities. Limnol. Oceanogr. 31:1072-1080.
Duarte, C.M. and Kalff J. 1990. Patterns in the Submerged Macrophyte Biomass of Lakes and the Importance of the Scale of Analysis in the interpretation. Can J Fish Aquat Sci. 47:357–363.
Hijmans, R.J. 2015. raster: Geographic Data Analysis and Modeling. R package version 2.5-2.
Johnson, J.A. and Newman, R.M. 2011. A comparison of two methods for sampling biomass of aquatic plants. J. Aquat. Plant Manage. 49:1-8.
Maceina, M.J. and Shireman, J.V. 1980. The use of a recording fathometer for determination of distribution and biomass of hydrilla. J. Aquat. Plant Manage. 18:34-39.
Maceina, M.J. and Shireman, J.V., Langeland, K.A. and Canfield J.R., D.E. 1984. Prediction of Submersed Plant Biomass by use of a Recording Fathometer. J. Aquat. Plant Manage. 22:35-38.
Madsen, J.D. 1993. Biomass Techniques for Monitoring and Assessing Control of Aquatic Vegetation. Lake and Reserv Manage. 7(2):141-154.
R Core Team. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Stent, C.J. and Hanley, S. 1985. A recording echo sounder for assessing submerged aquatic plant populations in shallow lakes. Aquat Bot 21:377–394.
Thomas, G. L., Thiesfeld, S.L., Bonar, S.A. et al. 1990. Estimation of submergent plant bed biovolume using acoustic range information. Can. J. Fish. Aquat. Sci. 47: 805-812.
Valley, R.D., Drake, M.T., and Anderson, C.S. 2005. Evaluation of alternative interpolation techniques for the mapping of remotely-sensed submersed vegetation abundance. Aquatic Botany. 81:13–25.

Valley, R.D., Johnson, M.B., Dustin, D.L. et al. 2015. Combining hydroacoustic and point-intercept survey methods to assess aquatic plant species abundance patterns and community dominance. J Aquat Plant Manage. 53:121–129.
Blog represents a summary of Master's thesis research by Andrew Howell; specifically Chapter 2: Correlation of Hydroacoustic Signature to Submersed Plant Biomass. Mr. Howell's thesis can be downloaded at

Monday, June 26, 2017


June 22nd 2017

Emanuela Ferina
Global Marketing Manager, C-MAP

Ray Valley
Aquatic Biologist & Biobase Product Expert 

Building on the Power of the BioBase Cloud Mapping Platform, New Product Generates Full Inventories of Shallow Water Habitats

C-MAP®, a leading supplier of digital navigation products to the maritime market, in partnership with a global leader in remote sensing services, EOMAP GmbH & Co KG, announced today the launch of EcoSat.

A new semi-automated wetland and coastal habitat mapping product that is part of the BioBase Cloud Mapping Platform, EcoSat uses the unique reflectance properties of vegetation and sea bottoms from high resolution satellite imagery and creates distinct polygon objects with spatial properties like area and perimeter. EcoSat's power is doubled when combined with its sister product EcoSound which uses sonar and GPS data files to map depth and submerged vegetation. EcoSat complements BioBase’s core functionality of submerged habitat mapping with sonar with new capabilities to inventory habitats in vast nearshore areas of aquatic environments. Aquatic habitat managers across the globe can use EcoSat to quickly assess and monitor changes in wetland complexes, shallow lakes, tidal estuaries and marshes, and benthic habitats. EcoSat will also be an invaluable tool for the assessment and monitoring of invasive aquatic plants. The Florida Fish and Wildlife Research Institute (FWRI) is currently using EcoSat and EcoSound to generate full aquatic vegetation inventories in high profile Florida lakes.

"The combination of the latest habitat image classification procedures and the high-performance of the BioBase Cloud environment brings significant benefits to all users that don’t have access to large data processing capacities," said Marcus Bindel, EOMAP data analyst.

Leveraging the expertise of a team of remote sensing experts at EOMAP, EcoSat rapidly processes raw satellite imagery and creates unique habitat classifications (e.g., polygons in a shapefile). Shapefiles and raw imagery – that are often hundreds of megabytes – are uploaded and processed by BioBase’s powerful cloud-based servers. Shapefiles and imagery are stored in a user's or organization's private online account for easy access and sharing. BioBase customers can interact with these detailed EcoSat files simply with any internet-enabled device. Users can also export custom charts of the EcoSat classifications to their Lowrance or Simrad chartplotter and navigate directly to a habitat of interest.

"BioBase is a first-of-its-kind, off-the-shelf cloud solution for organizations and businesses that need full aquatic habitat inventories quickly," said Greg Konig, head of product development, C-MAP. "Prior to BioBase automated mapping technologies, aquatic managers and researchers would spend countless hours at high costs just to produce a map. But not anymore."

For more information on C-MAP Light Marine and Commercial products, visit For more information about EcoSat and the BioBase Cloud Mapping Platform, visit

About C-MAP:
C-MAP is a world-leading provider of marine information with products ranging from electronic navigational charts to fleet management, vessel and voyage optimization. C-MAP offers the world’s largest marine navigation digital chart database, helping customers to address the complexity of maritime operations through integrated, intelligent information systems. For more information, visit

Processed polygons of emergent vegetation beds in Lake Tohopekaliga, FLfrom high resolution satellite imagery combined with submerged vegetation mapped with BioBase - EcoSound

Download automatically created Lowrance or Simrad Chart files from EcoSat and verify classifications directly from your watercraft  

Monday, May 1, 2017

Interpreting bottom hardness in shallow lakes and ponds: digging deeper into the data

BioBase's EcoSound bottom composition (hardness) algorithm has become quite popular for researchers and lake/pond managers to determine where sedimentation from the watershed may be occurring.  However, interpreting sonar returns in shallow environments (e.g., less than 7 ft or 2 m) with off-the-shelf sonar is challenging, especially if aquatic vegetation is present.  Each situation is different and the objective of this blog is to inform you of how to interpret your EcoSound map in situations when you encounter counter intuitive bottom hardness results.

Here are some high level points to remember.

EcoSound maps like the one shown in Figure 1 are statistically interpolated maps based on sonar returns directly below your boat.

EcoSound maps are spatial models based on point input data, not full bottom scans.  And just like regular statistical models, the type, quality, and amount of data going into the interpolation model (kriging) determine the quality and accuracy of the map output.  So, if you can't get a good sonar reading in a shallow, weedy bay, EcoSound may automatically "cleanse" the sonar return data (e.g., point data) during processing and the map produced (if any) may be based on insufficient input and not accurate.

There are a variety of reasons why data may be cleansed by EcoSound.  For bottom hardness, if you travel faster than 10 mph (16 km/h), or map bottoms shallower than 2.4 ft (0.74 m), or over vegetation greater than 60% biovolume (orange to red), bottom hardness points will not be produced.  So interpolated results may not expand over all covered areas or be extrapolated over areas that were cleansed.  We'll expand on this last point further...

Figure 1. An EcoSound vegetation (top) and bottom hardness map (bottom) from a shallow bay on the St. Lawrence River near Lake Ontario, USA.  Vegetation growing closer to the surface is indicated by red, and vegetation that grows closer to bottom is green. Red bottom hardness values indicate hard bottom scores, while tan colors indicate soft bottom scores. Transects were spaced 40-m and if not too shallow or weedy, bottom hardness data points were automatically created every 1-2 m based on the ~3 km/h speed.  Data points are actually aggregations of 5-30 transducer pulses from an approximately 1 m acoustic cone (e.g., 20 degree beam width in 1 m of water).
Bottom hardness values are not generated in dense vegetation beds which may be on soft bottoms, but are generated in gaps which may be hard.

Bottom composition is often one driver of whether aquatic plants can grow in lakes and ponds. Plants typically prefer relatively soft bottoms to hard bottoms.  But bottom returns from areas with dense plant growth extinguish the sonar signal and the ability to assess hardness.  Thus, EcoSound checks to see if vegetation is greater than 60% biovolume before processing the signal for bottom hardness.  Areas with dense vegetation get no hardness values, while a bare patch of gravel might get a value of "hard" (e.g., 0.4 to 0.5; Figure 2).

Figure 2. Cross-section of the 200 khz Sonar channel from a Lowrance .sl2 file from Thompson Bay St. Lawrence R. replayed on the Lowrance Simulator.

Figure 3. Top down view of bottom hardness for one transect in Thompson Bay (left), corresponding cross-section (right) and hardness point values (below).  Notice the hard spot in the middle of areas of dense vegetation growth that is presumed to be soft but cannot be confirmed based on the sonar reading.  Loading the data from the tabs in EcoSound pulls the coordinate data from the sonar track from the database, not the kriging grid data.
Figure 4. Bottom hardness point data exported from EcoSound and imported into ArcGIS.  The different colors indicate the bottom hardness scores.  Notice the large gap in the data where vegetation was the most dense (see Figures 1 and 5).

Figure 5. Bottom hardness point data overlain with vegetation point data > 60%. Notice the lack of overlap of data anywhere where dense vegetation occurred and hard scores where it didn't occur.

Interpolated (kriging) maps of bottom composition (hardness) maps may be biased toward hard scores in shallow or weedy lakes/ponds.

Above describes a situation where soft readings may not be recorded, but hard readings are. Consequently, the interpolated map might appear more hard than in real life.

Interpret maps and grid statistics from single transects with caution.

Recall, kriging predicts values in locations with no data based on locations where there is data.  Wherever you see the word "grid" in EcoSound reports and exports, this refers to kriging-derived data.  In contrast, like described above, "point" data are non-interpolated data collected directly below your vessel. Kriging does not care whether it is interpolating (ok), or extrapolating (generally not ok because we generally have low confidence of environments outside of our data range).
Figure 6. Example of kriging hardness grid points overlain with coordinate points exported from EcoSound and added to ArcGIS as a text event layer (WGS84 coordinate system). Kriging data values outside of the transects (extrapolated) may not be accurate. However, the map directly over the point values should be accurate.  In this example, it is recommended that the user only use the point hardness data and not the grid data.
Use point data from single transects, grid data when "back and forth" or "around and around" mapping.
Figure 7.  Bottom hardness from a river in Georgian Bay Lake Huron.  Surveyors only took one mapping pass.  Therefore, use the coordinate point data in any analysis.  Extrapolated grid data produced by kriging outside of the track may not be accurate. 
Figure 8.  Bottom hardness from a similar area in Figure 7.  Note the back and forth mapping passes.  In this case, the map may output may be more accurate over most of the mapped area and thus, we recommend using the interpolated kriging data for any analysis.  However, still exercise caution interpreting extrapolated data outside the track.
Bottom environments and true hardness is variable.  Use other tools to calibrate EcoSound bottom hardness outputs

EcoSound uses characteristics of the reflectivity of the bottom to infer whether the bottom could be soft, medium, or hard.  In general, sound signals reverberate strongly off of gravel and rocks and signal is absorbed into mud. Much independent test data  confirm a relationship between EcoSound-derived bottom hardness and true bottom hardness.

Most experienced biologists understand that bottom environments are rarely uniform or exhibit one extreme or another.  There are all sorts of substrates on the bottoms of lakes and ponds that could produce variability in hardness outputs (e.g., detritus layer, sand/silt/clay of various densities).  As such, we recommend that investigators take actual composition samples where possible, upload the waypoints to BioBase, and compare with EcoSound outputs (both point and grid).  In this way, the investigator can get a clearer view of what the composition map represents in real life.

Thursday, April 27, 2017

Helpful Resources for Getting Started with BioBase

BioBase is a powerful data collection tool for aquatic environments. To get the best results with BioBase - EcoSound, it is important to use proper data collection and management procedures. This post contains links to the resources that will help you get started with BioBase and get great data.

Our quality control team reviews every uploaded trip and looks for glaring issues with the trip like evidence of a slanted transducer, signal loss, poor signal quality. They may email you if they notice any significant issues with your trip, and suggest ways to fix the issue or ways to improve data quality before logging again. The quality control process may cause data edits and offsets to be lost and can “break” merges. Please allow one business day for quality control before applying these changes to your trips, or check the quality control review status by viewing a trip’s report. If there is a quality control reviewer’s name on the report, the trip has been reviewed. You can also see any comments that were not emailed to you on the report.

It is critically important to keep your Lowrance software updated. Software updates can be found here. Outdated software can result in inaccurate or lost data!

Our YouTube channel has many helpful videos, including data editing tutorials.

This post gives an overview on how EcoSound works along with some answers to frequently asked questions that many new users have.

The EcoSound Quick Start Guide shows recommended settings to use while logging sonar. Print this guide and keep it on your survey boat.

The EcoSound Support and Resources page has links to the EcoSound Full Operator's Guide as well as several tutorials, including guides for using EcoSound data in ArcMap.

If you ever need any assistance, contact the BioBase support team at

Friday, April 21, 2017

The BioBase you've come to love is now EcoSound!

What used to be known as BioBase will now be called EcoSound; a product name that better describes its function - using sound to characterize ecological environments.  We're not getting rid of the BioBase name; it's just going to mean a lot more!  Without changing function of the system, EcoSound uploads and merges will still be housed and displayed in a BioBase dashboard and on BioBase servers.  Soon BioBase will be getting an online face lift and users will have an easier time navigating to the information they need when they need it.  You've requested some changes and development is underway!

BioBase: The cloud platform re-positioned to support more than just sonar processing
With the move to C-MAP, BioBase is receiving a renewed focus to deliver the aquatic industry new and improved automated tools for the assessment of aquatic habitats.  In addition to renaming our sonar processing service EcoSound, the BioBase brand is being elevated to represent its primary role as a powerful cloud processing platform and a dashboard for visualization and analysis of a wide variety of spatial aquatic data.  BioBase will soon represent more than just an automated sonar mapping system.  More about this in a separate announcement coming soon!

Tuesday, January 31, 2017

Consumer Sonar for Bottom Mapping: Updated Reference List

Another FAQ we get is wondering if there are published studies using BioBase technology? There are many legacy applications on which the BioBase technology is based. Further, now that a sufficient passage of years has accumulated to support the "research to publication" cycle, we're happy to share several BioBase-specific studies published in the peer-reviewed literature.  This is far from an exhaustive list and we've intentionally left out the niche growth in consumer side-scan technology for creating habitat maps.  If there are good published papers you know of that are not on this list, please share in the comments.

Classic Literature
Duarte, C.M. 1987. Use of echosounder tracings to estimate the aboveground biomass of submerged plants in lakes. Canadian Journal of Fisheries and Aquatic Sciences 44: 732-735

Maceina, M and Shireman, J. 1980. The use of a recording fathometer for determination of distribution and biomass of Hydrilla. Journal of Aquatic Plant Management 18:34-39.

Maceina, M.J., Shireman, J.V., K.A. Langland, and D.E. Canfield Jr. 1984. Prediction of submerged plant biomass by use of a recording fathometer.  Journal of Aquatic PlantManagement 22: 35-38.

Stent, C.J. and Hanley, S. 1985. A recording echosounder for assessing submerged aquatic plant populations in shallow lakes. Aquatic Botany 21: 377-394

Thomas, G.L., Thiesfeld, S.L., Bonar, S.A., Crittenden, R.N., and Pauley, G.B. 1990. Estimation of submergent plant bed biovolume using acoustic range information. Canadian Journal of Fisheries and Aquatic Sciences 47: 805-812.

Recent Literature
Sánchez-Carnero, N., Rodríguez-Pérez, D., Couñago, E., Aceña, S., & Freire, J. 2012. Using vertical Sidescan Sonar as a tool for seagrass cartography. Estuarine, Coastal and Shelf Science 115: 334–344.

Meadows, GA 2013. A review of low cost underwater acoustic remote sensing for large freshwater systems. Journal of Great Lakes Research 39: 173-182.

Netherland, M. D., & Jones, K. D. 2015. A three-year evaluation of triclopyr for selective whole-bay management of Eurasian watermilfoil on Lake Minnetonka, Minnesota. Lake and Reservoir Management 31: 306–323. BioBase Paper

Radomski, P., & Holbrook, B.V. 2015. A comparison of two hydroacoustic methods for estimating submerged macrophyte distribution and abundance : A cautionary note. Journal of Aquatic Plant Management 53: 151–159. BioBase Paper

Valley, R. D., Johnson, M. B., Dustin, D. L., Jones, K. D., Lauenstein, M. R., & Nawrocki, J. (2015). Combining hydroacoustic and point-intercept survey methods to assess aquatic plant species abundance patterns and community dominance. Journal of Aquatic Plant Management 53: 121–129. BioBase Paper

Winfield, I. J., van Rijn, J., & Valley, R. D. 2015. Hydroacoustic quantification and assessment of spawning grounds of a lake salmonid in a eutrophicated water body. Ecological Informatics, 30, 235–240. BioBase Paper

Valley, R.D. 2016. Case Study Spatial and temporal variation of aquatic plant abundance : Quantifying change. Journal of Aquatic Plant Management 54: 95–101. BioBase Paper

Schooley, J.D. and B.C. Neely 2017. Estimation of paddlefish (Polyodon spathula Walbaum, 1792) spawning habitat availability with consumer-grade sonar. Journal of Applied Icthyology 00:1-9. . BioBase Paper

Friday, January 20, 2017

FAQ of the year: Does BioBase EcoSound Map Sediment Depth?

Thanks to advances in physical, chemical and biological technologies and funding that are focused on reducing sedimentation or muck depth in waterways, many water resource practitioners are eager to determine how much sediment is in a waterway of interest and how much could be removed. As such, we frequently are asked: "Will BioBase tell you how deep the sediment is?"

As much as we would like to say unequivocally "Yes!," and have an easy button solution, the reality is it's not that straightforward.  Off-the-shelf Lowrance transducers are designed to track the water sediment interface, especially at the standard 200 khz broadband frequency.  This blog discusses bottom tracking in a little more detail.  BioBase EcoSound algorithms do evaluate the acoustic reflectivity of bottom signal and will create a relative bottom hardness output.  Bottom hardness generally correlates with sediment depth but the relationship is variable depending on the system and we recommend users calibrate their bottom hardness outputs in their local system of interest.  The better approach in our opinion is knowing how deep the pond of interest should be and then comparing that to what the current conditions are to arrive at sediment depth.

Model Sediment Depth Based on Knowledge of the Desired Condition
Because BioBase EcoSound can produce a near real-time high-precision picture of current bathymetry, basic knowledge of the baseline/"as built" or desirable bathymetric design can be used to infer sediment depth and create highly precise, professional quality maps.  The Central Arizona Project's management of Arizona's primary aqueduct is probably the most widely cited example of this process.  Our optional GIS Services can take your BioBase EcoSound map and pre-treatment or baseline map and create a custom sediment depth map. See below for some great examples used by other lake management service providers.

Simulating sediment depth subtracting BioBase EcoSound-assessed bathymetry from "As Built" bathymetric design in a home owner association pond in CA. Work completed by Waterwork Industries, Windsor CA (  BioBase GIS Services conducted the analysis and created the custom map

Post Dredging Bathymetric Assessment conducted by Ecoresource Solutions, Inc., Arvada CO (  Mapped created by BioBase GIS Services.
Post Dredging Bathymetric Assessment conducted by Ecoresource Solutions, Inc., Arvada CO (  Mapped created by BioBase GIS Services.
Contact us if you are interested in learning more about how we can help you map sediment depth.

Wednesday, January 4, 2017

Announcement: BioBase Under New Ownership

In late 2016 Navico - the parent company of BioBase - was acquired by Goldman Sachs Merchant Banking Division and Altor Fund IV.  Also in 2016, Goldman Sachs and Altor acquired an industry leading marine cartography and services company called Digital Marine Solutions (DMS), which promotes their products and services under the C-MAP brand (  As of January 2017, BioBase and related digital services will be transferring from Navico to DMS (operating under the C-MAP brand).  We are working now on a new organizational structure and product roadmap that will establish DMS as the preeminent marine charting and cloud data services provider across both recreation and commercial sectors.   "...alongside the C-MAP brand we will now have a cloud-based infrastructure and range of web and mobile applications for recreational markets, such as Insight Genesis and BioBase which provide differentiated and high-quality crowd-sourced mapping services that lead the competition," stated Paul Ostergaard, Digital Marine Solutions Chairman. BioBase customers can expect the same high-level of service and minimum disruption with this change. Over the long run, the move to DMS and strategic company investments will bring new high-value features to BioBase and establish it as the aquatic and marine industry standard for processing, visualizing, and analyzing spatial data.  BioBase feature development and technical sales and support will still operate out of the same Minneapolis office where it was born and we anticipate an even faster development cycle in the coming years.  We hope to announce some great features that are already in the pipeline under this new structure and resulting roadmap.  Please direct any questions about this transition to Ray Valley ( or Matt Johnson (