One of BioBase’s strengths is its simplicity. You don’t need an advanced engineering degree in hydrography to make a high quality bathymetric map with an off-the-shelf sonar device. If you have your transducer installed correctly, settings correct on your Lowrance, and achieve good coverage on your waterbody of interest, then BioBase’s EcoSound algorithm will produce a very precise, high quality bathymetric map output within minutes of upload to biobasemaps.com. The speed and ease of bathymetric mapping wins the day for many of our users, but perhaps even more valuable, is the benchmark you are setting for an unknown day in the future when something has changed on the lake and you need to have some “historical” information to understand how much change has taken place
Use Case: Monitoring Sedimentation
One of our most frequently asked questions by new users is “will BioBase measure sediment thickness or the depth of the sludge?” This was a source of a recent blog. Interestingly, the answer is different depending on how long our customers have been using BioBase. For the user who has no prior information about how deep the lake or pond is supposed to be, BioBase may not provide detailed enough information about the actual thickness of the sediment (sediment depth is correlated with EcoSound hardness but it is highly variable; see this blog for further details). However, for the pond management consultant who happened to “BioBase” a client’s pond in 2013 while she happened to be on site for another matter and is now hearing from the client in 2019 that his pond is “filling in,” the answer about whether BioBase can tell him how much sediment has filled in is a most definite yes! For this pond consultant, it was a most fortuitous (or perhaps prudent?) thing that she decided to voluntarily map her clients pond in 2013. Now with a 2019 survey, she can precisely quantify exactly how much sediment has accumulated and where over the 7 years by doing a simple subtraction of the depth and water volume between surveys and comparing maps. The comparison of maps can be done a fancy GIS way like described in this blog. Or a quick and easy way through BioBase (see examples below).
The centralized nature of BioBase (biobasemaps.com) cloud technologies coupled with sophisticated, yet low-cost consumer electronics like Lowrance or Simrad depth sounders/chartplotters have created fertile grounds for developing, testing, and verifying algorithms for typing aquatic environments. The more users upload from a greater range of systems, the more refined algorithms can become addressing a wider range of conditions and use cases!
Early in 2014, we released a revision to our EcoSound bottom composition (hardness) algorithm that is more sensitive and robust in a greater range of depths and bottom conditions. Many outside researchers were involved with collecting important “ground truth” information while they logged their BioBase data. This blog not only serves to describe the new Bottom Composition algorithm, but also publish the results and acknowledge the scientists that helped with this effort.
We’re excited to announce the launch of the new biobasemaps.com website! You’ll find an image-rich professional look and feel as well as information segmentation into solutions and features that speak directly to the markets we serve (Aquatic Plants, Fisheries, Water Resources, Private Ponds, Coastal).
New visitors to biobasemaps.com will also see more information about our optional GIS Services that can take your BioBase maps and tailor them to your precise needs regarding image size, contour interval, custom legend, logos, etc.
Dabbling in GIS? We can help you get started in QGIS
Many of you also come to us with questions about how to do more with your BioBase outputs (e.g., custom contouring, water volume calculations, spatial data analyses). To empower you to fully leverage the potential of BioBase outputs we have prepared several step-by-step QGIS tutorials to get you the outputs you require for your work. See our Support Resources page for these tutorials along with other useful self help resources
I frequently get inquiries from current and prospective BioBase users about the accuracy of consumer-grade Lowrance GPS and whether survey-grade 3rd party receivers capable of differential correction (DGPS) or receiving positions from multiple satellite constellations (Global Navigation Satellite System – GNSS) could be used with Lowrance and processed with BioBase.
The first question about accuracy prompted a test in March of 2013 with a Lowrance HDS tested side-by-side with a Trimble GeoXH. I was pleased to find less than 1m deviation on average from post-processed Trimble DGPS positions. One meter accuracy and precision is typically sufficient for most boat-based mapping applications. Still, prerequisites for some projects require DGPS, and there are a number of BioBase users who have and still would prefer to have DGPS generated positions to use when logging trips. Thus, I was interested in exploring the capabilities of networking positions from a third-party receiver into a Lowrance HDS.
River channel thalwegs (the line of lowest elevation within a valley or watercourse) are often dynamic, and sometimes hidden features of large river systems. Especially low slope or impounded systems. The thalweg is a critical geomorphological feature of river and reservoir systems and affects everything from sediment transport, to fisheries habitat, to algae or invasive plant control.
Thus a good bathymetric contour map is a necessary pre-requisite for effective river and reservoir management. Here, we walk you through how to use new real time technologies (C-MAP’s Genesis Live) to produce smooth, precise, and accurate maps of hidden river thalwegs all within one trip to the site and with automated post-processing with BioBase’s EcoSound. We’ll use an annotated image gallery to take you through this process.
What is EcoSat?
EcoSat delivers a one-of-it’s-kind semi-automated cloud processing of very high resolution satellite imagery to map nearshore vegetation and coastal benthic habitats. EcoSat uses the latest multi-spectral imagery from reputable providers such as Digital Globe (World View 2,3 and 4), Airbus Defence and Space (Pleiades), and ESA’s Sentinel program and industry standard image processing techniques. Sophisticated Amazon Web Service cloud infrastructure rapidly processes imagery, creates reports and imagery tiles, and delivers detailed habitat maps to user’s BioBase dashboard where it can be analyzed and shared. Average turnaround time from imagery tasking order to delivery of results is 60 days. The rapid and standard processing methods are allowing entities like the Florida Fish and Wildlife Conservation Commission to establish regular monitoring programs for emergent vegetation. The extremely long and expensive one-off nature of conventional remote sensing mapping projects using non-repeatable tailored techniques has prevented natural resource entities from assessing the degree that habitats are changing as a result of environmental stressors such as invasive species invasions and climate change.
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 impact 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. We recommend that you opt into processing alerts in My Account so you are always alerted of processing status and any quality control issues with your trips or merges
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!
This post gives you a more in depth look at how EcoSound works. This blog shares some tips and tricks along with some answers to frequently asked questions that many new users have.
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 QGIS. The EcoSound Quick Start Guide shows recommended settings to use on your Lowrance or Simrad while logging sonar. Please follow this quick start guide carefully.
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 here. Contact 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