PROFESSIONAL PAPER SJ2006-PP3 

DISTRIBUTION OF SUBMERGED AQUATIC

VEGETATION IN THE LOWER ST. JOHNS RIVER: 

2004 ATLAS
Professional Paper SJ2006-PP3 

DISTRIBUTION OF SUBMERGED AQUATIC VEGETATION

IN THE LOWER ST. JOHNS RIVER: 2004 ATLAS

by 

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

St. Johns River Water Management District 

Palatka, Florida 

2006 

1

Professional Paper SJ2006-PP3 

DISTRIBUTION OF SUBMERGED AQUATIC VEGETATION

IN THE LOWER ST. JOHNS RIVER: 2004 ATLAS

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

ABSTRACT 

This atlas contains Lower St. Johns River Basin submerged aquatic vegetation (SAV) data for the years 2003 

and 2004. Ground-truthing transects were performed and mapped for each year. Hyperspectral imagery 

was obtained and mapped for only 2003. 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, 2,140 acres of SAV were mapped within the mainstem of 

the lower basin. 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 

(Scheffer 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flats (D. Dobberfuhl, unpublished 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 other areas of the river and are 

critically important to the overall 

health of the system. 

 The current SAV mapping effort 

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 
2

approximately 50% of the 

ground-truthing transects 

(Dobberfuhl 2002). Photo 

interpretation and delineation of the 

2001 imagery was performed on 

computer monitors by using scanned 

aerial imagery. 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. 

 This edition of the SAV atlas 

includes data for the year 2003 and 

2004. Two independent measures of 

SAV spatial extent were performed 

in 2003 and compared to provide an 

accuracy estimate for hyperspectral 

imagery classification. These data 

and maps are presented as a 

continuing series of atlases 

documenting distribution and 

change of SAV in the LSJRB. 

METHODS 

Ground-Truth Transects 

 Seventy-five sites were selected 

to ground-truth the aerial surveys. 

At each site, one transect was run 

from the shoreline to beyond the 

terminus of the SAV bed. The lineintercept method was used to record 

species present and percent cover; 

transect length was recorded, and 

the two endpoints for each transect 

were recorded by using geographic 

information system (GPS). Transect 

length and the georeference of 

endpoints were entered into GIS. 

Transect length was compared to 

spatial coverages derived from 

hyperspectral image classification to 

assess the relative accuracy of the 

classified images. 

Hyperspectral Image Analysis 

 Imagery—The LSJRB received 67 

hyperspectral swaths that covered 

the lower St. Johns River shoreline in 

addition to the shoreline of Crescent 

Lake (Figure 1). The imagery was 

collected September 13–18, 2003 

(HDI, Halifax, Nova Scotia, Canada), 

using a compact airborne 

spectrographic imager (CASI) sensor 

(ITRES, Calgary, Alberta, Canada). 

The resulting images have 2-meter 

(m) pixels and a swath width of 512 

pixels, or 1024 m, and swath length 

of 10 to 12 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. Five of the 67 images 

were used to develop the 

classification methodology. 

 Several processes were run on the 

images before delivery by the 

vendor, including radiometric 

correction, across-track illumination 

correction, geocorrection, and 

correction to DOQs. Radiometric 

correction procedures compensate 
3

Figure 1. Location of 67 raw images and five pilot study images 

Figure 2. Images 30a (top) and 49 
4

for variations in optical transmission 

and sensor sensitivity. This process 

also removes remnants from 

electronic offset, frame shift smear, 

and scattered light and calibrates the 

pixel digital numbers (DN) to 

Spectral radiance units (SRU), with 

SRU equal to one micro-watt per 

square centimeter steradian 

nanometer, which also can be 

written 

SRU = 1 μWcm-2

sr

-1

nm

-1. 

Across-track illumination correction 

is performed to minimize the 

distortion and increase in DN values 

that may occur near the edges of a 

flightline. The images were 

geocorrected by using data collected 

from onboard GPS and gyroscopic 

equipment to calculate ground 

position. The images were also 

geocorrected to 2004 (ADS-40 

airborne imagery) set of 1 m digital 

orthophoto quarter-quadrangles 

(DOQQs) to produce a product with 

tighter geodetic control. The DOQQS 

are in UTM (zone 17), NAD83 

(HARN) coordinates, which 

according to St. Johns River Water 

Management District (SJRWMD 

2005), agrees with the 2–4 m of the 

1999 U.S. Geological Survey (USGS) 

DOQQ set, which has a positional 

accuracy standard of ± 10 m for 90% 

of well-defined features (USGS 

2004). 

 Spectral Bands and 

Bandwidths—Hyperspectral data 

was collected in 19 different bands of 

varying widths across the visible 

(400 [nanometer] nm to 700 nm) and 

near infrared (700 nm to 1300 nm) 

range of the electromagnetic 

spectrum (Table 1). Each band 

collects information in a different 

portion 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. 

Table 1. Band ranges and bandwidths 

Band Range Bandwidth 

(nm) 

1 442.1 ± 10.3 nm 20.6 

2 464.3 ± 10.4 nm 20.8 

3 487.7 ± 13.2 nm 26.4 

4 508.0 ± 7.6 nm 15.2 

5 524.9 ± 7.6 nm 15.2 

6 541.7 ± 7.7 nm 15.4 

7 554.8 ± 5.8 nm 11.6 

8 565.2 ± 4.9 nm 9.8 

9 574.6 ± 4.9 nm 9.8 

10 584.9 ± 5.8 nm 11.6 

11 595.3 ± 4.9 nm 9.8 

12 604.7 ± 4.9 nm 9.8 

13 615.1 ± 5.8 nm 11.6 

14 627.4 ± 4.9 nm 9.8 

15 654.9 ± 5.8 nm 11.6 

16 684.3 ± 4.9 nm 9.8 

17 696.7 ± 7.8 nm 15.6 

18 715.8 ± 9.7 nm 19.4 

19 845.0 ± 20.3 nm 40.6 

nm = nanometers 

 Image Processing—ERDAS 

Imagine (Leica Geosystems, 

Norcross, Ga.) and ENVI (Research 
5

Systems Inc., Boulder, Colo.) were 

used for image processing. Imagine 

has the ability to handle and process 

very large image files while ENVI 

has strengths in dealing with 

hyperspectral imagery. ArcView and 

ArcGIS (Environmental Systems 

Research Institute, Redlands, Calif.) 

were used for GIS work. 

 Before creating a classification 

scheme, each individual image 

swath was equalization-stretched 

and calibrated to create uniformity 

throughout the images. A radiance 

equalization-stretch was developed 

to correct for lingering swath-toswath variation, and a reflectance 

calibration methodology was 

developed to calibrate remotely 

sensed spectra to ground-collected 

spectra. This process permits 

comparisons between images and 

allows spectral signatures collected 

from one image to be applied to 

other images. 

 Equalization-Stretch—Images 

were given a radiance equalizationstretch to reduce between-swath 

variations resulting from 

illumination differences. Image 29-2 

was selected to be the primary swath 

since it would also serve as the basis 

of the following calibration efforts. 

The range of secondary swaths 

(images 30a, 31, 49, and 56b) were 

compared to the primary swath, and 

a linear function was developed for 

each band of the secondary swath, 

such as 

SRn,i = An,i * DNi + Bn,I 

whereSRn,i is the stretched radiance 

in band i, DNi is the digital number 

for band i, and An,i and Bn,i are the 

slope and intercept coefficients for 

band i. In essence, the secondary 

swath is squeezed or stretched (via 

slope) to match the range of the 

parent swath and is then shifted 

higher or lower from its origin to 

that of the primary swath (via 

intercept). 

 Calibration—After equalizationstretch, images were calibrated 

based on the known reflectance of 

two targets found in image 29-2: a 

bright target (white sand parking lot) 

and a dark target (deep open-river 

water). Reflectance measurements of 

these two targets had been collected 

at the time of the 2003 airborne data 

collection using a spectro-radiometer 

at previously identified sites. These 

measurements were then convolved 

to match the spectral ranges of the 19 

bands of the imagery. The brighttarget DN statistical extract was 

obtained from the region of interest 

(ROI) on the image, while the DN 

statistical extract of dark river water 

was obtained from a representative 

dark-target ROI. A linear calibration 

equation was then developed for 

each band, such as 

SRi

 = Ai * DNi + Bi

where SRi is the spectral reflectance 

in band i, DNi is the digital number 

for band i, and Ai and Bi are the 

slope and intercept coefficients for 

band i. The calibration was applied 

to all of the images, including the 
6

unstretched primary swath, image 

29-2. 

 Classification—Classifying an 

image involves giving each pixel in 

the image a class designation based 

on its value across all bands. There 

are two major types of classification 

schemes: unsupervised and 

supervised. Unsupervised 

classifications allow the computer 

processing 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 similarities 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 (SAM) 

classification scheme. SAM is a 

physically based spectral 

classification scheme that uses an ndimensional 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 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 transect data 

that had been collected along the 

river in 2003. The lower basin of the 

St. Johns River has 20 permanent 

monitoring sites where GPS-based 

transect data are collected annually. 

Each of the four secondary swaths 

covers one of the permanent 

monitoring sites, and each site has 

five transects within a 50-meter 

length of the shoreline, where 

detailed data is collected concerning 

the species, plant density, and 

canopy height of the vegetation the 

entire length of the bed (Figure 3). 

With this information, regions of 

interest (ROIs) were digitized onscreen so that polygons in the 

hyperspectral image could be 

captured to show medium-to-high 

density SAV beds. Likewise, 

additional ROIs were digitized to 

capture the spectral signatures of 

other class types, including urban 

areas, tree cover, and water in each 
7

of the four secondary swaths 

(Figure 4). 

 Spectral Signatures—Using the 

digitized ROIs, the spectral 

signatures of all 19 bands for the four 

class types can be compared. 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. Across all four secondary 

images, bands 1 through 17 show 

little or no difference in the 

reflectance values of water and SAV. 

However, band 18 shows a 

distinctive dip for water and a 

Figure 3. Transects used for classification from image 49 

Figure 4. Regions of interest for four classes in image 49 
8

significant peak for SAV (Figures 5a 

and 5b). Of the 19 bands available in 

this data set, the spectral separation 

of water and SAV is dependent on 

this single band and allows for the 

differentiation of water and SAV. 

Figure 5a. Spectral signatures of four classes in image 30a 

Figure 5b. Spectral signatures of four classes in image 56b 

 Spectral signatures were also 

collected in the field with a 

spectroradiometer (GER1500, Group 

for Environmental Research, 

http://www.ger.com/1500.html) in 

the fall of 2005, to improve the SAM 

classification scheme and to make 

suggestions for band placement in 

the next round of data collection. 

These ground-based signatures were 
9

imported to a spreadsheet and 

convolved to match the 19 unique 

bands of the imagery. Spectral 

reflectance information was collected 

for dense eelgrass (Vallisneria 

americana), algal blooms (Microcystis 

spp.), dense waterlily (Nymphaea 

spp.), hydrilla (Hydrilla verticillata), 

periphyton, sand bottom at 

approximately 20-cm depth, mud 

bottom at approximately 2-m depth, 

and open water. 

 Spectral Library—A spectral 

library is a collection of reflectance 

spectra for natural and/or 

man-made materials for use in 

identifying unknown spectra. For the 

images in the pilot study, the 

signatures collected in the field (22 

signatures) were added to the four 

signatures for each class collected 

from each of the four images (16 

signatures) to create a unique 

spectral library for the St. Johns 

River, consisting of 38 signatures for 

both aquatic and terrestrial features. 

This spectral library was referenced 

by ENVI to classify the images using 

the SAM model. 

RESULTS AND DISCUSSION 

 The accuracy of the hyperspectral 

imagery classification was assessed 

by comparing SAV bed width 

(perpendicular to the shoreline) from 

classified hyperspectral imagery to 

bed lengths measured from groundtruthing transects in the river. The 

spatial accuracy of the imagery was 

much lower along the edges of the 

imagery. Therefore, transects located 

near the edges were not used for the 

accuracy assessment if nearby 

ground features (e.g., docks, 

shoreline) were not in good spatial 

agreement with the referenced 

DOQQs. Fifty ground-truthing 

transects were found to be within the 

spatially accurate portions of the 

hyperspectral images and were used 

for measuring classification 

accuracy. 

 Bed lengths for the 50 selected 

ground-truthing transects from 

2003 were compared to the bed 

lengths estimated from the 2003 

hyperspectral imagery at the same 

locations. Overall, the hyperspectral 

imagery underestimated bed lengths 

at the 50 transect sites by 22%. 

However, among individual 

transects, there were large 

differences between estimates based 

on hyperspectral imagery and field 

measurements. 

 SAV areas mapped from the 

classified hyperspectral imagery 

show 2,140 acres in the mainstem of 

the river, from Palatka north to the 

Fuller Warren Bridge in Jacksonville. 

This estimate is lower than those 

generated during other years and 

from other methods (Dobberfuhl 

2002, Dobberfuhl and Trahan 2003). 

However, if the 22% underestimate 

is considered, that coverage can be 

revised up to 2,611 acres. This is still 

low, but 2003 was a relatively severe 

drought year, and grass bed 

recession was common in many 

areas of the river (Sagan 2005). 

Another factor influencing this 

estimate is the method of grass bed 
10

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. It 

should be noted, because of these 

types of issues, using different 

analytical methods comparisons of 

areal coverage between years is 

problematic. 

 The differences between the 

estimated and measured data 

occurred for several reasons. First, 

there was a large degree of variation 

in quality from image to image. 

Because standardized spectral 

libraries were used to classify each 

image, SAV may not have been 

correctly identified in lower-quality 

images. Second, short grass in deep 

water defies detection. Deep 

overlying water attenuates the 

reflected light energy available to the 

sensor, and there may not have been 

sufficient sensor sensitivity under 

those conditions. Finally, of the 19 

bands originally chosen for the 

imagery, only two demonstrated any 

usefulness in identifying the 

presence or absence of SAV. 

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