Digital Acoustic System for Ecosystem Monitoring and Mapping: Assessment of Fish, Plankton, Submersed Aquatic Vegetation, and Bottom Substrata

Classification

J.C. Hoffmana, J. Burczynskib, B. Sabolc, and M. Heilmand

a Virginia Institute of Marine Science, Gloucester Point, Virginia 23062-1346, USA, email: jhoffman@vims.edu b BioSonics, Inc., 4027 Leary Way NW, Seattle, Washington 98107, USA, email: janusz@BioSonicsInc.com

c US Army Corps of Engineers Waterways Experiment Station, CEERD-EE-C, 3909 Halls Ferry Rd, Vicksburg, Mississippi 39180-6199, USA, email:sabolb@wes.army.mil

d ReMetrix LLC, 11550 N. Meridian Suite 600, Carmel, Indiana 46032-4565, USA, email: doug@remetrix.com

Summary

Underwater acoustic technology provides powerful tools for determining size, abundance, and distribution of both fish and plankton. Recent developments in classifying bottom substrates and quantifying underwater vegetation make hydroacoustics an effective tool for monitoring and mapping habitat parameters in aquatic ecosystems. This study incorporated hydroacoustic sampling for bathymetry, substrate type, underwater vegetation, and fish distribution in Lake Washington, Seattle, USA. Data were collected using three independent echo sounder systems, to maximize data accuracy and vessel use, and geo-referenced using a Differential Global Positioning System enabling the acoustic data to be used in a Geographic Information System. Results indicated that aquatic vegetation was limited to depths less than 8 m, four different bottom substrates were identified, fish density was generally low (0.1 m-3) and patchy. This method proved to be an effective way to study various habitat influences on fish distribution, as well as map and monitor important physical and seasonal habitat parameters such as bathymetry, bottom character, and aquatic vegetation distribution.

1. Introduction

Recent advances in seabed classification [10] and submersed aquatic vegetation (SAV) detection [8] using acoustic technology have made it possible to collect acoustic data on bathymetry, substrata type, plant coverage and height, and fish abundance and distribution. However, each type of acoustic application has optimal beam-width, frequency and pulse-width requirements for data collection. For example, when working in shallow water environments (0-100 m), bottom classification will require a low frequency, wide-beam echo sounder system; aquatic vegetation will require a high frequency, narrow-beam echo sounder system; and fish data collection will require a 38-420 kHz, narrow-beam echo sounder. This may appears to expand the time and effort required to collect and process multiple types of acoustic data. However, by using multiple digital echo sounder systems, the

entire data set can be collected in a single acoustic field survey, greatly enhancing the speed in which the data may be acquired.

Acoustic Data Analysis. Two recently developed software packages, BioSonics’ EcoSAV® and VBT Seabed Classifier, were used for data analysis. The processing parameters and analysis techniques used by these two packages are relevant to our findings, as they determined the primary considerations for sample design of the study. Those considerations included the ability of the two software packages to produce spatial data on a similar sampling scale and to produce data sets that could be geospatially modeled with a limited number of assumptions.

EcoSAV is designed to quantify SAV from hydroacoustic data. The software requires a digital echo sounder (BioSonics), differential GPS (DGPS) and computer for data acquisition. The echo sounder system collects and stores the digital echo

signal from the echo sounder (either split- or single-beam) and DGPS data (latitude, longitude and time). EcoSAV is used to process the digital echo signal to extract SAV information. The processing algorithms in EcoSAV determine bottom depth, plant presence/absence, plant height, and aerial coverage [8].

The algorithm analyzes a ping cycle (8-10 pings per cycle) between two consecutive DGPS signals. It estimates the bottom depth and determines whether plants are present or absent for each ping in the cycle by examining the shape of the echo envelope. If plants are present, the distance from the bottom signal to the top of the plant canopy is determined. Data are summarized for each cycle, including the mid-DGPS point, the middle ping number, the bottom depth, the average aerial coverage, and the average plant height. The limit of patch-size detection is established by the beam-width of the echo sounder; narrower-beams will have greater resolution than wider-beams at the same water depth. The spatial-scale of the data is the size of a ping cycle, a function of the ping rate, DGPS update rate, and vessel speed.

The VBT Seabed Classifier software is designed for use with data collected from a single beam digital echosounder. VBT can process and analyze data files of the digitized echo envelope and classify bottom types by comparing an unknown sample to a verified reference sample (e.g. gravel). The time integral of the squared amplitude of the digitized echo envelope with 20 Log (R) time-varied gain is the basic algorithm of the echo signal processing. Bottom typing methods implemented in VBT are: First Echo Normalization [7], First/Second Bottom Ratio [6,3], First Echo Division [9] and Fractal Dimension [10, 4].

The fractal dimension method, in which a fractal dimension is assigned to the shape of the echo envelope, has been shown to be successful for typing bottom substrata [11] and was utilized for analysis of the acoustic data from this study. Data output includes longitude, latitude and bottom type, data necessary for use in a GIS package. The spatial-scale of the data is determined by setting the processing parameters; data can therefore be output on the same scale as EcoSAV (i.e. average data over 8 pings, 10 pings, etc.).

The goals of the study were to demonstrate that the various acoustic data could be quickly collected, processed, analyzed, and input into GIS, and to test the ability to integrate the data given the different scales at which the data may be relevant to

fish habitat. In order to address the first goal, it was necessary to develop a sampling methodology that utilized a variety of echo sounder systems. In order to address the second goal, it was necessary to undertake a fine-scale sampling design that maximized the resolution of the acoustic data, which could be collected in reasonable sampling time.

2. Methods Hydrographic tools, hydroacoustic methods, and

specialized software were utilized to collect, analyze, and map ecosystem data on Lake Washington, in Seattle, Washington. We surveyed the north end of Lake Washington at the in-flow of the Sammamish River. Lake Washington has a deep, narrow, glacially carved basin (mean depth 32.9 m; max depth 65.2 m; length 21 km). It is a relatively clear mesotrophic lake (mean epilimnion depth 10 m) supporting a variety of native and exotic fish.

We focused on the north end of Lake Washington because it is bathymetrically complex, has large SAV meadows, and is a potential nursery for young-of-year fish. For sediment and plant mapping, survey transects were planned utilizing a collection of USGS digital orthoquadrangles of the Lake Washington Area. Parallel transects were run roughly east-west at intervals of 50m. Coordinates for transect endpoints were exported into commercial navigation software for transect navigation. The study area was separated into two sections based on survey time; each section required approximately six hours to survey, or about 20 east-west transects. One diagonal transect extended from a northern to a southern corner of each section. East-west transects were used for bathymetry, bottom-substrate type, and SAV coverage data collection. Diagonal transects were used for fish abundance data collection in order to avoid horizontal or vertical bias [5].

A survey map was designed with a commercial navigation software package using BSB format maps produced by the United States National Oceanic and Atmospheric Association (NOAA). The navigation software was operated simultaneously with the hydroacoustic acquisition software in order to collect data along pre-established transects. The signal from an onboard Differential Global Positioning System (DGPS) was fed into both the navigation package and acoustic data collection software so that all data

collected were concurrently geo-referenced. Upon completion of the data acquisition, we processed the data to extract bathymetry, bottom substrate, SAV height and cover, and fish abundance and distribution. Accurate two-dimensional ground measurements (e.g., length, area) required that the DGPS data be mathematically transformed into a map projection system appropriate to the area of interest. All other data was subsequently imported into a GIS database for integration and mapping.

We collected fish abundance and distribution data using a BioSonics DE 420 kHz, 6º split-beam echo sounder set to 0.4 ms pulse-width, 4 pings s-1, and threshold of –90 dB. Data were collected at a boat speed of 4.5 –5.0 km hr-1.

Once the fish data were obtained, we collected data on bathymetry and substrate type using a BioSonics DE 70 kHz, 6º single-beam echo sounder set to 0.4 ms pulse-width, 5 pings s-1, and a threshold of –70 dB. We collected SAV distribution data using a DE 420 kHz, 6º split-beam echo sounder set to 0.1 ms pulse-width, 5 pings s-1, and a threshold of –130 dB. These two echo sounder systems were deployed side-by-side at 0.5m depth. The two systems were operated independently, from separate on-board laptop computers, with separate DGPS units. This maximized our control of the two systems and their respective parameters for data collection. The 70 kHz system was always operated; the 420 kHz system was only operated in depths less than 10 m. Data were collected at 4 km h-1.

BioSonics’ Visual Analyzer software was used to determine fish distribution for every section, integrated over all depths for every ca. 100 m distance. A threshold of –60 dB was used to exclude backscatter from plankton and boat wake.

EcoSAV was used to determine the bathymetry (70 kHz data; see [8] for methods), and plant coverage and height (420 kHz single-beam data). Bottom depths were corrected for the depth of the transducer deployment. In order to estimate plant coverage and height in areas where the aquatic vegetation extended to the face of the transducer, the quality filters that normally exclude these data were turned off, the height was conservatively assumed to be equal to the transducer distance from bottom, and the data were verified manually by examining the echogram from the region of interest. The detection limit was increased to provide a conservative estimated of plant coverage (set to 8 increments; default is 4), and the required region of low noise above the plant signal reduced

to allow for greater vegetation height (set to 7 increments; default is 10). EcoSAV calculations of plant height, water depth, and percent bottom coverage were transformed into percent plant biovolume: the percentage of water column with vegetation at a given report location. This transformation was performed by multiplying percent bottom cover by the ratio of plant height to water depth.

VBT Seabed Classifier was used to determine bottom type, using the Fractal Dimension method (for the 70 kHz data). After completion of the bathymetric analysis and mapping, regions of potentially different bottom substrate were identified based upon assumptions regarding bathymetrically related substrate distribution. Survey transects were selected and then examined using VBT without any pre-determined substrate categories. Areas of similar physical (proximity and depth) and acoustic characteristics were identified using the fuzzy logic analysis tools [1]. Heavily vegetated areas were not examined because analysis of the acoustic characteristics of bottom types in these areas is problematic.

Areas of similar depth, location, and closely clustered acoustic characteristics (energy and fractal dimension of the first bottom echo) were assigned classification identifications. This identification was associated with the range of values that were contained within the bounding box created using the fuzzy clustering analysis. This method approximates the concept of “unsupervised classification”, where areas producing similar measurements are classified as a bottom type, without assigning specific physical characteristics to the class. This analysis identifies adjacent sample regions that are unlike each other. Examination of several regions resulted in the selection of four bounding boxes for bottom classification. Three of those types were located in a single transect, which was used as the “reference” for bottom types 1-3 and bottom type 4 came from a different transect.

GIS operations were performed using a combination of ArcView 3.2 and TNTmips 6.5. Processed data points from EcoSAV, VBT, and Visual Analyzer echo integration were reprojected from latitude/longitude coordinate system to the native projection and datum of the USGS orthoquadrangle imagery (Projection: Universal Transverse Mercator Zone 10 North, Datum: North American Datum 1983). Reprojected coordinates

were overlaid with imagery and planned survey transects to confirm proper projection.

3. Results Geospatial surface models were developed from

final data on percent plant biovolume, percent bottom cover, sediment type, and bathymetry. Plant and bathymetric data were modeled using a minimum curvature method, while sediment data were modeled using an ordinary kriging approach. Reports classified by VBT as undefined (13.5% of total reports) were selectively removed before geospatial modeling. The raster cell size of each resulting surface model was 64 m2. The final area analyzed for bathymetry, sediment, and plant characteristics equaled 249.2 hectares.

The dominant bathymetric feature was a long shipping channel that extended from the very north end of the lake to deep water (Figure 1). A shallow shelf was evident on the north end of the lake, as well as a small alluvial deposition zone at the outflow of the Sammamish River. Approximately 1 km south of the river outflow, the lake quickly deepened and the basin sides became much steeper, greatly limiting the distribution of aquatic vegetation. In general, the sampling area was shallow and the depth did not exceed 15 m.

Figure 1. Bathymetric contours and fish density for north Lake Washington

Aquatic vegetation was distributed widely and covered almost 60% of the survey area, but was confined to depths less than 8 m, presumably due to light-limitation (Table 1). The highest density patches reached 100% cover, were relatively uniform in cover, and were most common in water less than 3 m deep. Only two patchy habitats with widely varying cover (1-100%) were observed; the first was near the outflow of the Sammamish River and the second in an intrusion of deeper water into the northeast corner of the lake (Figure 1). Table 1. Aerial distribution of aquatic vegetation coverage by bottom cover for north Lake Washington

By biovolume, the greatest proportion of the area surveyed had plants that were between 1-19% of the water column (Table 2). Three patches with high biovolume were observed: the northeast corner, the corner, and along the west side of the area surveyed (Figure 2). Table 2. Aerial distribution of aquatic vegetation coverage by biovolume for north Lake Washington

BioVolume Area (Hectares)

% Total

No Plants Detected

100.7 40.4%

1 - 19% 106.6 42.8%

20 - 39% 34.1 13.7%

40 – 60% 7.8 3.1%

TOTAL 249.2

Four common sediment types were observed in regions where SAV was absent. By sediment type,

Bottom Cover Area

(Hectares)

% Total

No Plants Detected

100.7 40.4%

1 - 19% 18.0 7.2%

20 - 39% 21.2 8.5%

40 - 59% 23.9 9.6%

60 - 79% 26.9 10.8%

80 - 100% 58.5 23.5%

TOTAL 249.2

Figure 2. Percent aquatic vegetation biovolume for north Lake Washington

most common (42.9%; Table 3). Three of the sediment types were associated with specific regions of the survey area (Figure 3). Sediment type 2 was the dominant sediment type in the northeast corner of the region analyzed. Sediment type 3 was dominant along the west side of the area. Sediment type 4 was dominant in the southeast corner. Sediment type 1 was not a dominant type and was distributed throughout the middle of the survey region. Table 3. Aerial distribution of sediment types for north Lake Washington

Sediment Type Area (Hectares)

% Total

1 3.99 4.1%

2 41.41 42.9%

3 31.81 32.9%

4 19.39 20.1%

TOTAL 96.6

The survey transect for evaluation of fish density through echo integration was 2.63 km in length. The report positions noted on the attached map are

at intervals ranging from 120-180 m, with an average interval distance of 146 m. Fish occupying both pelagic and benthic habitat were detected, but species identification was not possible due to the sampling methodology. Fish density was generally low (<0.1 m-3) and patchy (Figure 1). The few areas of high density were located on the far ends of the southern diagonal transect and in the middle of the northern diagonal transect. Targets in the southern section were unimodally distributed (strong peak at –38 dB), while targets in Section 1 were bimodally distributed (peaks at –36 dB and –44 dB).

4. Discussion The fine scale of the survey transects allowed us

to collect high-resolution acoustic data for analyzing the basin bathymetry, substrate types, and distribution of aquatic vegetation. We recognize that such fine scale work is not always possible, but emphasize that many of the patch sizes we observed could have been missed by surveying with transects spaced 100 m to 500 m apart. Analyzing both aquatic vegetation and bottom substrate data on a similar spatial scale (10 pings) was possible, but demonstrated an inherent trade-off in spatial resolution with variation in substrate type classification. Averaging over fewer pings has the possibility of leading to a greater number of unclassified regions in areas where the echo signal demonstrates greater variability. A similar problem does not arise in the EcoSAV software because cover is not averaged over the ping cycle.

Aquatic vegetation was easily recognized by the 420kHz echo sounder system. The area is known to contain both milfoil and elodea, but we did not attempt to separate the two different species during analysis of the acoustic data. The high frequency system performed significantly better than the 70 kHz system; using the 70 kHz system to define boundaries for aquatic vegetation yielded a difference in placement of the boundary of over 100 m.

The 70 kHz system was not ideal for bottom type analysis due to the narrow-beam width. However, the four bottom types analyzed were relatively robust and successfully applied to over 85% of the acoustic signals within the survey area. Because

the system is relatively shallow, we were not concerned with depth-related corrections to the acoustic signal from a specific bottom type.

Figure 3. Contour map of sediment classification for north Lake Washington

Although targets were not extensively analyzed for horizontal or vertical distribution, the bi-modal frequency distribution suggests that more than one size or species was detected. Though fish abundance was generally low, the regions of high density may represent valuable edge habitat. However, because data was integrated over all depths, the shallow region of high density in the southwest corner may represent a similar fish density per area.

Overall, the study demonstrated that the various echo sounder systems could be combined to yield a wealth of data on aquatic habitat and fish distribution. Further studies would ideally include fish sampling to obtain species and length distributions, as well as bottom grabs to identify the various substrate types. Integrating the data in GIS was an effective way to evaluate fish distribution in regards to bathymetric features, substrate types, and patches of aquatic vegetation. In this study, fish density did not appear to be associated with a specific bottom type or amount of vegetation, but more extensive studies of Lake Washington during different seasons may yield more definitive relationships.

Acknowledgements Tim Acker, BioSonics, Inc. generously

contributed boat and equipment time to the effort. Brian McFadden and Edward Kudera, both of BioSonics, Inc., assisted in sampling and data analysis. References

[1] J.C. Bezdek: Pattern recognition with fuzzy objective function algorithms, New York, Plenum Press, 1981.

[2] BioSonics, Inc. DT/DE Series Users Manual Version 4.02, Seattle, Washington, 2000.

[3] R.C. Chivers, N. Emerson, D.R. Burns: New acoustic processing for underwater surveying, The Hydrographic Journal 56(1990) pp. 8-17.

[4] Z. Lubniewski, A. Stepnowski: Sea bottom typing using fractal dimensions, Proceedings of the International Symposium on Hydroacoustics and Ultrasonics, Gdansk-Jurata, 1997, pp. 12-16.

[5] D.N. MacLennan, E.J. Simmonds: Fisheries Acoustics, London, Chapman & Hall, 1992, ch. 7, pp. 221-227.

[6] A. Orlowski: Application of multiple echoes energy measurements for evaluation of sea bottom type, Ocenologia 19(1984) pp. 61-78.

[7] E. Pouliquen, X. Lourton: Sea bed classification using echo sounder signal, Proceedings of the European Conference on Underwater Acoustics, Luxembourg, 1992, pp. 535-538.

[8] B.M. Sabol, R.E. Melton: Development of an automated system for detection and mapping of submersed aquatic vegetation with hydroacoustic and global positioning system technologies, report I: the Submersed Aquatic Vegetation Early Warning System (SAVEWS) – system description and user’s guide (Version 1.0), USACE Waterways Experiment Station, Vicksburg, Mississippi, 1990.

[9] A. Stepnowski, D. Bakiera, M. Moszynski: Analysis and simulation of hydroacoustic methods of sea bed classification, raport badawczy 52/95, Politechnika Gdanska, Gdansk, 1995.

[10] A. Stepnowski, D. Bakiera, M. Moszynski, J. Burczynski: Visual real time Bottom Typing System (VBTS) and neural network experiment for sea bed classification, Proceedings of the 3rd European Conference on Underwater Acoustics, Heraklion, 1996, pp. 24-38.

[11] J. Tegowski, Z. Lubniewski: The use of fractal properties of echo signals for acoustical classification of bottom sediments, Acustica 86(2000) pp. 276-282.