PROFESSIONAL PAPER SJ2009-PP2 

DISTRIBUTION OF SUBMERGED 

AQUATIC VEGETATION IN THE 

LOWER ST. JOHNS RIVER: 2006 ATLAS
Professional Paper SJ2009-PP2 

DISTRIBUTION OF SUBMERGED AQUATIC VEGETATION

IN THE LOWER ST. JOHNS RIVER: 2006 ATLAS

Dean R. Dobberfuhl, Ph.D. 

Courtney E. Hart 

St. Johns River Water Management District 

Palatka, Florida 

2009 

Professional Paper SJ2009-PP2 

DISTRIBUTION OF SUBMERGED AQUATIC VEGETATION

IN THE LOWER ST. JOHNS RIVER: 2006 ATLAS

Dean R. Dobberfuhl, Ph.D., and Courtney E. Hart 

ABSTRACT 

This atlas contains Lower St. Johns River Basin submerged aquatic vegetation (SAV) data for 2006. 

Ground-truthing transects and ground-truthing polygons from field-collected data were mapped and 

assessed, and hyperspectral imagery was collected and analyzed. Hyperspectral imagery classification 

was generally successful and appeared to identify a greater proportion of the existing SAV than previous 

attempts using true-color photo interpretation. Based on the imagery classification, 1,273 acres of SAV 

were mapped within the mainstem of the lower basin and Doctors Lake. In some cases, imagery 

classification and transect data gave divergent bed-size estimates. Reasons for those discrepancies are 

briefly discussed. 

INTRODUCTION 

Submerged aquatic vegetation (SAV) plays an 

important role in many aquatic ecosystems, 

particularly in shallow lakes (Sheffer 1998). It is 

important for anchoring sediment, sequestering 

nutrients, serving as a food resource and as 

nursery habitat (Dennison et al., 1993). SAV is 

an important food source and habitat for a 

number of organisms in the Lower St. Johns 

River Basin (LSJRB). Manatees graze the 

deepwater edge of the SAV beds and use the 

shallow areas for cover (reviewed in Burns et al. 

1997). Fish rely on SAV beds for predator 

evasion and increased foraging opportunities 

(Rozas and Odum 1988, Heck and Crowder 

1991, Jordan et al. 1996), as do insects (Batzer 

and Wissinger 1996, Lombardo 1997, Solimini 

et al. 1997). SAV beds in the LSJRB have threefold-higher fish abundance and 15-fold-higher 

invertebrate abundance than do adjacent sand 

flats (D. Dobberfuhl, unpublished data). Thus 

SAV-based production rates are 

disproportionately higher than unvegetated areas 

of the river and are critically important to the 

overall health of the system. 

SAV mapping efforts began in 1995 to evaluate 

the quality and distribution of estuarine and 

freshwater submerged habitats. This ongoing 

effort is designed to quantify ecosystem change 

in response to restoration efforts and to provide 

information necessary for the development of 

pollutant load reduction goals and total 

maximum daily loads. 

True-color aerial imagery was obtained in 1998 

and 2001. Photo interpretation and delineation of 

the grass beds in the 1998 imagery 

underestimated grass bed extent at 

approximately 50% of the ground-truthing 

transects (Dobberfuhl 2002). Photo 

interpretation and delineation of the 2001 

imagery was performed by on-screen digitizing 

SAV identified from scanned orthorectified truecolor aerial photographs. This methodology was 

unsuccessful and failed to identify SAV in many 

areas of the river (Dobberfuhl and Trahan 2003). 

In 2003, the decision was made to obtain 

hyperspectral imagery, focusing on those 

spectral bands thought to be characteristic of 

important vegetative and benthic features. This 

imagery appears to be more sensitive than the 

true-color imagery previously obtained and 

better characterizes grass bed features. Imagery 

was again collected in 2006, with the bands for 

data collection altered somewhat from the 2003 

band selection in order to provide more spectral 

separation between features. In fact, fewer bands 

3 
were selected in 2006, but the selected bands 

had narrower bandwidths and their placement 

along the electromagnetic spectrum allowed for 

better targeting of desired features. 

This edition of the SAV atlas includes groundtruthing SAV extent for 2005 and groundtruthing SAV extent and SAV presence and 

absence as identified by hyperspectral imagery 

analysis for 2006. Two independent measures of 

SAV spatial extent were performed in 2006 and 

compared to provide an accuracy estimate for 

hyperspectral imagery classification. These 

maps are presented as a continuing series of 

atlases documenting the distribution and change 

of SAV in the LSJRB. 

METHODS 

Ground-Truthing Transects 

In 2005 and 2006, 75 sites were selected to 

ground-truth actual SAV conditions. At each 

site, one transect was run from the shoreline to 

beyond the terminus of the SAV bed. The lineintercept method (Canfield 1941) was used to 

record species present and percent cover; 

transect length was recorded, and the two end 

points for each transect were recorded by using 

global positioning system (GPS). Transect 

length and the georeference of end points were 

entered into GIS. Transect length was compared 

to the spatial coverage derived from 

hyperspectral image classification to assess the 

relative accuracy of the classified images. Of the 

75 ground-truthing transects, 50 overlapped the 

hyperspectral images spatially and were used to 

assess spatial accuracy of the classified imagery. 

Ground-Truthing Polygons 

In 2006, 107 polygons along the shoreline were 

delineated and surveyed. Data included species 

present, aggregate percent coverage, plant foliar 

density, water depth, mean plant height, and 

substrate type. These polygons were then 

randomly split into two groups — 54 to be used 

as training data in developing a classification 

scheme to apply towards all images and 47 

polygons were to be used during the accuracy 

assessment. Six polygons were not used at all: 

three polygons were collected outside of the 

imagery extent and three polygons that 

identified emergent vegetation were not 

examined in the study. 

Hyperspectral Image Analysis 

Imagery — The LSJRB received 87 

hyperspectral swaths that covered the lower St. 

Johns River shoreline (Figure 1). The imagery 

was collected April 13–16, 2006 (HIL, 

Elmsdale, N.S., Canada), using a compact 

airborne spectrographic imager (CASI) sensor 

(ITRES, Calgary, Alberta, Canada). The 

resulting images have 1.2-meter (m) pixels and a 

swath width of 512 pixels (or 614.4 m) and 

swath length generally ranging from 5 to 8 

kilometers (km) (Figure 2). Data collection was 

timed to prevent shadows from riverside trees 

from interfering with the sensor’s view of the 

water and SAV near shore. Twelve of the 87 

images were used to develop the classification 

methodology and 75 images were used in the 

final classification (12 images were excluded 

completely because of image overlap and 

redundancy). 

Several processes were run on the images before 

delivery by the vendor, including image data 

processing, attitude data processing, GPS data 

processing, merging attitude and position data, 

atmospheric correction, and geocorrection. 

Image data processing involves a radiometric 

correction of the raw sensor image data that 

compensates for variations in optical 

transmission (“noise”) and sensor sensitivity. 

Attitude data and GPS data processing are 

necessary to standardize the images. These two 

data files are then merged, synchronized, and 

corrected if necessary. The imagery is then 

atmospherically corrected to remove the effects 

of scattering and absorption of sunlight by 

particles in the air. This process permits 

comparisons between images and allows spectral 

signatures collected from one image to be 

applied to other images. The final step is to 

apply the GPS navigation data to the 

radiometrically and atmospherically corrected 

imagery to create a geocorrected version of the 

4 
Figure 1. Location of 87 raw images 

 

5 
6 

Figure 2. Image 61 
imagery. The images were geocorrected by 

using data collected from onboard inertial 

measurement unit (IMU) and surface collected 

ground control points (GCPs) bringing the 

geocorrected images within a 2-pixel, or 

approximately 2.4-m, spatial accuracy (HIL 

2006). 

Spectral Resolution — Spectral resolution 

describes “the number and width of the portions 

of the electromagnetic spectrum measured by 

the sensor” (Govender 2007). For this study 

hyperspectral data was collected in 17 different 

bands of varying widths across the visible (400 

nanometer [nm] to 750 nm) and near infrared 

(750nm to 2,500 nm) range of the 

electromagnetic spectrum (Table 1). Each band 

Table 1. Band ranges and bandwidths 

Band Range Bandwidth

(nm) 

1 453.6 +/– 5.8 nm 11.6 

2 465.7 +/– 6.7 nm 13.5 

3 480.6 +/– 4.8 nm 9.6 

4 491.7 +/– 4.8 nm 9.6 

5 542.0 +/– 4.8 nm 9.6 

6 572.7 +/– 3.9 nm 7.8 

7 581.1 +/– 3.0 nm 6.0 

8 589.5 +/– 2.0 nm 4.0 

9 681.7 +/– 2.0 nm 4.0 

10 693.0 +/– 2.0 nm 4.0 

11 698.7 +/– 2.1 nm 4.2 

12 710.2 +/– 2.1 nm 4.2 

13 720.6 +/– 3.0 nm 6.0 

14 736.0 +/– 3.0 nm 6.0 

15 750.4 +/– 4.0 nm 8.0 

16 762.9 +/– 5.0 nm 10.0 

17 787.1 +/– 4.0 nm 8.0 

nm = nanometers 

collects information in a different part of the 

electromagnetic spectrum and, therefore, may be 

able to contribute to identifying unique features 

on the earth’s surface. Most objects reflect 

energy in unique patterns across the 

electromagnetic spectrum, and bands and 

bandwidths should be selected to allow that 

variation to appear without creating 

unmanageable amounts of data. Water absorbs 

most wavelengths in the near infrared, so the 

majority of the most useful information 

regarding SAV will come from the visible range. 

Image Processing — ENVI Geospatial 

Software (ITT Visual Information Solutions, 

Boulder, Colo.) was used for image processing. 

ENVI has the ability to process and analyze 

geospatial imagery including spectral signature 

selection and image classification. ArcGIS 

(Environmental Systems Research Institute, 

Redlands, Calif.) was used for Geographic 

Information Systems (GIS) work. 

Classification — Classifying an image involves 

giving each pixel in the image a class 

designation based on its value across all bands, 

known as its spectral signature. There are two 

major types of classification schemes: 

unsupervised and supervised. Unsupervised 

classifications allow the computer that processes 

the data to identify classes based purely on the 

digital number (DN) of each pixel in each band. 

Each individual pixel is compared to other 

pixels, and they are then divided into clusters 

based on the similarity of their DN combination 

values. The objective is to group spectral 

response patterns into clusters that are 

statistically separable. Supervised classifications 

are generally more accurate because they

include analyst-specified spectral information 

developed from specified locations in the image. 

Training sites are identified by the user, and the 

spectral signatures from those areas guide the 

model as it classifies the remaining pixels in the 

image into the designated class types. 

For this project, we selected a type of supervised 

classification scheme uniquely developed for use 

with hyperspectral data — the spectral angle 

mapper classification scheme or SAM. SAM is a 

physically based spectral classification scheme 

that uses an n-dimensional angle to match pixels 

to reference spectra. The algorithm used in the 

SAM scheme determines the similarity between 

two spectra by determining the “spectral angle” 

between them. It uses the direction of the spectra 

and not its length or magnitude; therefore, all 

illumination possibilities are treated equally. 

When used with calibrated data this method is 

relatively insensitive to illumination and albedo 

effects. With SAM, smaller angles represent 

7 
8 

closer matches of pixels to reference spectra; 

however, any pixels that do not fall into the 

angle range of any of the classes will remain 

unclassed. 

Training Data — Training data for the SAM 

classification scheme were created with the use 

of ground-truthing polygons that were collected 

along the shoreline over several days in April 

2006 and within 2 weeks of the image 

acquisition. These polygons were overlaid onto 

the imagery and pixels were selected from 

within the polygons that represented areas of 

medium-to-high density SAV or bare bottom 

areas with no SAV. Additional pixels were 

selected from several of the images to capture 

the spectral signatures of other class types 

including urban areas, tree cover, and open 

water. The signatures from the selected pixels 

were then included in the spectral library. 

Spectral Signatures — The spectral signatures 

of all pixels across 17 bands for the four class 

types (SAV, bare, urban, terrestrial vegetation) 

can be compared, and distinct differences can be 

seen in the shape of the signature curve between 

class types (Figure 3). Urban areas and areas 

with tree cover are spectrally distinct and are 

easily distinguished. However it is more 

important, and more difficult, to find spectral 

separability between water and SAV. 

Spectral Library — A spectral library is a 

collection of reflectance spectra for natural 

and/or man-made materials for use in identifying 

unknown spectra. A unique spectral library for 

the lower basin of the St. Johns River consisting 

of 63 signatures for both aquatic and terrestrial 

features was developed (Figure 4). This spectral 

library was referenced by ENVI to classify the 

each individual image using the SAM model. 

RESULTS AND DISCUSSION 

The accuracy of the hyperspectral imagery 

classification was assessed with the use of the 

two different ground-truthing data sets. First, the 

remaining 47 ground-truthing polygons were 

examined and the classification accuracy for 

each polygon was determined by comparing the 

polygon’s class designation to the image’s 

classification. If an area was classified as SAV 

in the polygon but only 95% of the same area 

was classified as SAV in the imagery, the 

accuracy for that polygon was 95%. Several 

metrics were useful in evaluating the overall 

accuracy of the classification based on these 

polygons. The average of the presence/absence 

accuracy for all 47 polygons is 78%, but the 

median value of the accuracy values is 95%. The 

average of the presence/absence accuracy of the 

50% trimmed mean of the measured values is 

90% (Table 2). 

Table 2. Accuracy assessment 

Accuracy 

Metrics 

GroundTruthing 

Polygons 

GroundTruthing

Transects 

Mean 78% 82.1% 

Median 95% 89.9% 

50% trimmed mean 90% 86.0% 

The second method used to determine accuracy 

involved a comparison between SAV bed length 

as identified by the classified hyperspectral 

imagery to bed lengths measured from groundtruthing transects in the river. Fifty of the 75 

ground-truthing transects were found to be 

within the extent of collected imagery and were 

used for measuring classification accuracy. A 

comparison of bed length measurements at the 

50 transect sites shows that the hyperspectral 

imagery classified an average of 82.1% of the 

SAV extent measured by the ground-truthing 

transects, a median accuracy value of 89.9%, 

and the average of the 50% trimmed mean of 

measured values is 86%. These results suggest 

that the hyperspectral classification generally 

underestimated bed length. Closer examination 

reveals that the deep end of the SAV bed was 

more often the underestimated area. Deep 

overlying water attenuates the reflected light 

energy available to the sensor, and it is possible 

that there may not be sufficient sensor sensitivity 

under those conditions. These results suggest 

that the hyperspectral sensor had more difficulty 

detecting SAV in deeper water condition, 

especially if those plants were short. 
9 
10 
The classified hyperspectral imagery identifies 

1,202 acres of SAV in the mainstem of the St. 

Johns River and 71 acres in Doctors Lake for a 

total of 1,273 acres. This estimate is lower than 

the 2,140 acres estimated using the 2003 

imagery and earlier transect data (Dobberfuhl 

2002, Dobberfuhl and Trahan 2003). Several 

factors are likely responsible for the observed 

reduction in coverage. 

One factor influencing the reduced estimate is 

the method of grass bed delineation. With 

traditional photo interpretation, small bare areas 

within grass beds are not as likely to be 

identified as in hyperspectral imagery 

classification. Thus the larger number of bare 

patches, or “holes,” will tend to decrease the 

aggregate areal coverage estimate. 

A second factor is differences in shoreline 

delineation. A 2003 image analysis used 

shoreline boundaries derived from SJRWMD 

DOQ imagery, since spatial accuracy of the HSI 

imagery was too low to identify the shoreline. In 

contrast, 2006 HSI imagery had much higher 

spatial accuracy and the shoreline boundary was 

generated from the imagery itself. Differences in 

shoreline boundary between 2003 and 2006 may 

account for a small proportion of the overall loss 

in coverage. It should be noted that because of 

these types of issues using different analytical 

methods to compare areal coverage, results 

between years should not be compared in light 

of analytical errors. 

The final factor relating to the reduction in 

coverage is not imagery-related but water 

quality-related. Drought conditions persisted 

from fall 2003 through fall 2004, during the 

winter/spring of 2005, and again during 

winter/spring of 2006. The consequence of these 

repeated drought conditions was unusually high 

salinity values that reduced or eliminated SAV 

from many downstream areas in the lower basin. 

Field measurements of SAV coverage 

corroborate these reductions. Therefore, a large 

fraction of the SAV reduction observed in the 

imagery between 2003 and 2006 is a real and 

verifiable change. 

Hyperspectral imagery continues to be a useful 

tool in identifying the presence and absence of 

SAV in the St. Johns River. As technology 

improves and analysts gain more experience in 

data collection and analysis, it is expected that 

hyperspectral imagery analysis will become 

more accurate and yield more detailed SAV 

maps in the future. For future hyperspectral 

analyses, additional questions may be addressed 

such as determining the maximum depth at 

which hyperspectral imagery can accurately 

identify SAV or determining whether or not 

hyperspectral imagery can accurately 

differentiate between SAV species. In future 

monitoring efforts, it may be possible to 

accurately map individual species’ distributions 

with only a minimum in-water effort. 

REFERENCES 

Batzer, D.P., and S.A. Wissinger. 1996. Ecology 

of insect communities in nontidal wetlands. 

Annual Review of Entomology 41: 75–100. 

Burns, J.W., Jr., A.D. Chapman, E. Messer, and 

J. Konwinski. 1997. Submerged aquatic 

vegetation of the lower St. Johns River. 

Palatka, Fla.: St. Johns River Water 

Management District. 

Canfield, R.H., 1941. Application of the line 

interception method in sampling range 

vegetation. Journal of Forestry 39: 388–94. 

Dennison, W.C., R.J. Orth, K.A. Moore, J.C. 

Stevenson, V. Carter, S. Kollar, P.W. 

Bergstrom, R.A. Batiuk. 1993 Assessing 

water quality with submerged aquatic 

vegetation. BioScience 43: 86–94. 

Dobberfuhl, D.R. 2002. Distribution of 

submerged aquatic vegetation in the lower 

St. Johns River: 1998 atlas. Professional 

paper 2002-1. Palatka, Fla.: St. Johns River 

Water Management District. 

11 
12 

Dobberfuhl, D.R., and N. Trahan. 2003. 

Distribution of submerged aquatic 

vegetation in the lower St. Johns River: 

2001 atlas. Professional paper 2003-1. 

Palatka, Fla.: St. Johns River Water 

Management District. 

Govender, M., K. Chetty, and H. Bulcock. 2007. 

A review of hyperspectral remote sensing 

and its application in vegetation and water 

resource studies. Water SA 33(2): 145–51. 

Heck, K.L., and L.B. Crowder. 1991. Habitat 

structure and predator-prey interactions in 

vegetated aquatic systems. In Habitat 

structure: the physical arrangement of 

objects in space, S.S. Bell, E.D. McCoy, 

and J. Mushinsky, eds., pp. 281–99. New 

York: Chapman Hall. 

Jordan, F., M. Bartolini, C. Nelson, P.E. 

Patterson, and H.L. Soulen. 1996. Risk of 

predation affects habitat selection by the 

pinfish Lagodon rhomboides (Linaeus). 

Journal of Experimental Marine Biology 

and Ecology. 208: 45–56. 

Lombardo, P. 1997. Predation by Enallagma

nymphs (Odonata: zygoptera) under 

different conditions of spatial 

heterogeneity. Hydrobiologia. 356: 1–9. 

Rozas, L.P., and W.E. Odum. 1988. Occupation 

of submerged aquatic vegetation by fishes: 

testing the roles of food and refuge. 

Oecologia. 77: 101–06. 

Sagan, J.J. 2005. A reanalysis of data related to 

submerged aquatic vegetation within the 

lower St. Johns River: 1996–2005. Final 

report. Palatka, Fla.: St. Johns River Water 

Management District. 

Scheffer, M. 1998. Ecology of shallow lakes. 

London: Chapman Hall. 

Solimini, A.G., G.A. Tarallo, and G. Carchini. 

1997. Life history and species composition 

of the damselfly assemblage along the 

urban tract of a river in central Italy. 

Hydrobiologia 356: 21–32.