Analysis of Space Borne SAR Data for Wetland Mapping in Virginia Riparian Ecosystems

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Analysis of space-borne SAR data for wetland mapping in Virginia

riparian ecosystemsL. L. Bourgeau-Chaveza; E. S. Kasischkea; S. M. Brunzella; J. P. Mudda; K. B. Smitha; A. L. Fricka

a Environmental Research Institute of Michigan, PO Box 134001, Ann Arbor, MI 48113-4001, USA.

To cite this Article Bourgeau-Chavez, L. L. , Kasischke, E. S. , Brunzell, S. M. , Mudd, J. P. , Smith, K. B. and Frick, A.L.(2001) 'Analysis of space-borne SAR data for wetland mapping in Virginia riparian ecosystems', International Journalof Remote Sensing, 22: 18, 3665 — 3687

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int. j. remote sensing, 2001, vol. 22, no. 18, 3665–3687

Analysis of space-borne SAR data for wetland mapping in Virginia

riparian ecosystems

L. L. BOURGEAU-CHAVEZ*, E. S. KASISCHKE,

S. M. BRUNZELL, J. P. MUDD, K. B. SMITH and A. L. FRICK†

Environmental Research Institute of Michigan, PO Box 134001, Ann Arbor,MI 48113-4001, USA

(Received 29 March 1999; in nal form 24 July 2000 )

Abstract. In this study the utility of NASA’s Shuttle Imaging Radar-C (SIR-C)data are evaluated for wetland mapping and monitoring. The fully polarimetricL- and C-band data are used in hierarchical analysis and maximum likelihoodclassication techniques. Each map produced is compared with the EnvironmentalProtection Agency’s Multi Resolution Land Characteristics classication for pixel-to-pixel accuracy assessment. Results show that both L- and C- band are necessaryfor detection of ooding beneath vegetated canopies. HH- polarization is foundin this study and others to be better than VV for wetland discrimination. Thecross-polarizations (HV or VH) are needed for discrimination of woody versusherbaceous vegetation.

1. Introduction

Increasing pressures have been put on wetland resources by the high demand for

land development and continuing pollution problems. The need for management

and protection of these valuable ecosystems, as well as the need to understand

wetland ecology, have spurred the investigation of new technologies for mapping

and monitoring wetlands. Traditionally, the mapping of vegetation, including wet-

lands, has been conducted with optical data. One limitation on the use of optical

data for wetland mapping is their inability to penetrate vegetation canopies, and

thus their inability to remotely sense ooding beneath a closed canopy. Ancillary

data such as topography and soils as well as intensive eld surveys are often necessary

when using optical data for wetland mapping.

Since the early 1980s, L-band ( 23 cm wavelength) synthetic aperture radar (SAR)

systems have been known to penetrate vegetation canopies and receive enhanced

backscatter from forested and emergent wetlands (Ormsby et al. 1985, Richards et al.

1987). Recently, studies have shown C-band (5.7 cm wavelength) VV-polarizationSAR data from the European ERS satellite to be useful for detecting and monitoring

*e-mail: [email protected]† Present address: USGS Center for Coastal Geology, 600 Fourth Street South, St

Petersburg, FL 33701, USA.

International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online © 2001 Taylor & Francis Ltd

http://www.tandf.co.uk/ journals

DOI: 10.1080/01431160010029174

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L . L . Bourgeau-Chavez et al.3666

herbaceous and sparsely forested wetlands in Florida (Tanis et al. 1994, Bourgeau-

Chavez et al. 1996, Kasischke and Bourgeau-Chavez 1997, Smith et al. 2000, unpub-

lished). These studies demonstrated the ability to monitor extent of ooding and

hydroperiod in a variety of emergent wetlands using ERS data. Other studies have

been conducted to determine the utility of multi-polarization/multi-frequency data,

including NASA’s Shuttle Imaging Radar-C (SIR-C) and AirSAR for wetland classi-cation in tropical landscapes (Pope et al. 1994, 1997, Hess et al. 1995). These studies

demonstrate d the utility of multi-band data and polarimetric analyses ( HH-VV phase

diV erence) for mapping wetlands in tropical regions.

The purpose of the current study was to test the abilities of multi-frequency/

multi-polarization SAR data for wetland delineation and vegetation mapping in the

forested and herbaceous wetlands of the Rappahannock and York river systems in

south-eastern Virginia (gure 1). Our objective was to evaluate the utility of the six

bands of Shuttle Imaging Radar-C (SIR-C) data (L-HH, L-HV, L-VV, C-HH, C-HV, C-VV), as well as frequency/polarization combinations currently available in

existing (ERS, Radarsat, JERS) and planned satellites (Envisat, Radarsat II), which

may be used together to produce an accurate wetland map.

This study was primarily retrospective in design because it began in 1997 and

focused on SIR-C data collected in 1994 without coincident eld surveys. Field

surveys made in 1997 were collected during the same seasons (spring and autumn)

as the SIR-C missions.

The Virginia study area was dened by the SIR-C swath, which has been over-

laid on the Environmental Protection Agency’s (EPA) Multi Resolution LandCharacteristics (MRLC) vegetation classication in gure 1. The MRLC shows the

dominance of forested and emergent wetlands on the borders of the rivers. These

wetlands are part of a large expanse of riparian ecosystems that exist in the south-eastern United States, many of which have been drained and cleared for agricultural

use. While most of the wetlands within our test area are tidally inuenced, the

salinity levels vary depending on where the site is along the river. The further a site

is upriver from Chesapeake Bay, the lower the salinity. The salinity, in turn, aV ects

the type of ecosystem and species that develop at a particular site. The Rappahannock

river sites within our study area are dominated by brackish emergent marshes whilethe sites on the Mattaponi and Pamunkey rivers (York river system) are freshwater

ecosystems dominated by bottomland hardwood swamps.

The SIR-C data used in this study have an incidence angle of 25°, which is similar

to ERS (23°). This incidence angle was chosen because radar theory and experimental

studies have found steeper incidence angles to be optimal for detecting ooding

beneath a canopy (especially forested canopies; Ulaby et al. 1982, Richards et al.

1987, Wang et al. 1995). Although the SIR-C swath (gure 1) was continuous across

the three rivers of our study area, the SIR-C data were processed in 50 km scenes.

This resulted in one SIR-C scene containing the Rappahannock river (gure 2(a)

and (b)) and a second containing the Mattaponi and Pamunkey rivers (gure 2(c)

and (d )). Two dates of imagery were available with the 25° incidence angle,

17 April and 9 October 1994.

2. Methods

2.1. SAR data and preliminary evaluation of SIR-C data

All SIR-C data were processed and calibrated by EROS Data Center in Single

Look Complex format. The calibration techniques are described by Freeman et al.

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Space-borne SAR data for wetland mapping 3667

Figure 1. Map of Virginia study area including the Rappahannock and York ( Mattaponi,Pamunky and Chickahominy rivers) river systems with SIR-C swath overlaid. Thebase map is the EPA’s MRLC vegetation classication.

(1995). Calibration accuracies obtained were 2.3 dB for L-band and 2.2 dB for C-

band in April; 2.0 dB for L-band and 3.2 dB for C-band in October. The data were

then ground range corrected using standard algorithms and georeferenced using

ERDAS Imagine software and ground control points with accuracies within one

pixel. There was no rainfall on the days of the overpasses and no signicant rainfall

in the few days prior to the overpasses.

A preliminary assessment of the utility of the SIR-C data for wetland delineation

was conducted by producing several three band false colour image composites. These

composites were then compared with existing wetland maps of the study area and

with eld observations. This initial assessment was done to determine the feasibility

of the study and to aid in site selection for eld observation.

The three band combinations chosen for presentation in the SIR-C images of

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F i g u r e 2 .

T h r e e b a n d S I R - C R G B c o m p o s i t e s ( C - H

V ,

L - H V ,

C - H H ) o f t h e R a

p p a h a n n o c k R i v e r ,

V i r g i n i a ,

f r o m ( a ) 1 7 A p r i l a n d ( b ) 9 O c t o b e r 1 9 9 4 .

T h r e e B a n d S I R -

C

R G B c o m p o s i t e s ( L - H H

, C - H H ,

C - V V ) o f t h e M

a t t a p o n i a n d P a m u n k e y R

i v e r s , V i r g i n i a ,

f r o m

( c ) 1

7 A p r i l a n d ( d )

9 O c t o b e r 1 9 9 4 .

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gure 2 enhance the delineation of the various wetlands while minimizing confusion

with other vegetation types (such as forested upland). Figure 2(a) and (b) presents

two C-HV, L-HV, C-HH composites of the Rappahannock river, one from the April

overpass, and one from the October 1994 overpass. The pink areas along the river

and its tributaries are wetlands dominated by big cordgrass (Spartina cynosuroides).

The green areas are upland forest, and purple areas are agriculture. The agricultureand emergent wetlands appear more similar in October than in April, probably due

to similarities in the state of the vegetation of both the wetlands and agricultural

elds in October. While the agricultural elds were dark in the April C-HH image

they were similar to the wetlands in the October C-HH image. Substituting L-HH

for C-HH in this composite allowed for better diV erentiation of agriculture from

emergent wetland in October. In October, at C-HH the agriculture and emergents

were equally bright while at L-HH the agriculture was dark due to low return, and

the emergents were grey with moderate return.The bright white areas in the northern part of the April Rappahannock image

(gure 2(a)) are forested wetlands. The tidally inuenced forested wetlands (at the

top of the main tributary heading north) stay bright in October while the non-tidal

forested wetlands (to the west of these) are no longer visible and are probably dry

(gure 2(b)).

Two L-HH, C-HH, C-VV SIR-C band combinations are presented in gure 2(c)

and (d ) for the April and October imagery of the Mattaponi and Pamunkey rivers.

This simulated JERS (L-HH), Radarsat (C-HH), ERS (C-VV) combination per-

formed quite well at delineating the forested wetlands, which appear bright whitealong the rivers. The C-HV, L-HV, C-HH combination used for the Rappahannock

scene did not do as well in delineating the forested wetlands of the Mattaponi and

Pamunkey rivers. Some very small herbaceous wetlands occur at the easternmostpart of the Mattaponi River (see insets of gure 2(c) and (d )). Most of these sites are

islands in the Mattaponi River which appear green in colour in April and cyan in

October, similar to the agriculture which surrounds the rivers.

These preliminary observations demonstrate that the SIR-C data have potential

to be used to delineate wetlands and to diV erentiate between wetland types as well

as other vegetative cover.

2.2. Site selection and description

Thirteen wetland sites were chosen along the Rappahannock, Mattaponi and

Pamunkey rivers which were visible in the SIR-C swaths from data takes 129.2 and

145.1 of 17 April 1994 and 9 October 1994, respectively (gure 1). Sites were chosen

to represent the types of wetlands that exist in this region and also to understand

the range of backscatter being observed from the wetlands in the SIR-C images. The

main wetland types along these rivers are red maple (Acer rubrum) dominated

bottomland hardwood swamps, big cordgrass (S. cynosuroides) dominated wetlands,

and sweet ag (Acorus calamus) and arrow arum (Peltandra virginica) dominated

wetlands. Our intent was to choose multiple sites of each vegetation type along

the rivers. We used the MRLC, SIR-C imagery, and marsh inventories of the

Rappahannock, Mattaponi and Pamunkey rivers from 1979 to 1990 ( Doumlele 1979,

Silberhorn and Zacherle 1987, Priest et al. 1987, Priest and Dewing 1990) to choose

a number of potential sites. We then conducted eld reconnaissance to evaluate the

potential sites. Criteria for site selection included areas of homogeneous vegetation

cover and areas which were at least 200 m×

200m in size. Since much of the land

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along these rivers is privately owned, our nal site selection relied upon obtaining

permission to access the land. We worked with the Mattaponi/Pamunkey River

Association and government agencies to contact landowners and obtain permissions.

This site selection process resulted in four herbaceous sites along the

Rappahannock, three herbaceous and four forested sites along the Mattaponi, and

two forested sites along the Pamunkey. Figure 3 contains photographs of typical

Figure 3. Ground photos of (a) Mattaponi River site 3 emergent wetland in April and (b)October. (c) Rappahannock River site 4 emergent wetland in April and (d ) October.(e) Chickahominy River site 2 medium-density forest wetland. ( f ) Pamunkey site 3

high-density forested wetland.

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emergent and forested wetlands from the three rivers. An example of the type of

emergent wetland (sweetag- and arrow arum-dominated wetland) found on the

Mattaponi River is seen in gure 3(a) and (b). Photos showing how the site appeared

in April (gure 3(a)) and October 1997 (gure 3(b)) are presented. The Mattaponi

site is dominated by herbaceous species that die in the autumn and decompose over

the winter months leaving mud ats in the spring. The diversity of species in thespring is lower than that of the autumn, and the dominant species change. The

vegetation is nearly a metre tall in October and is very dense (table 1).

An example of a Rappahannock emergent site is shown in gure 3(c) and (d ),

with photos from April and October, respectively. The Rappahannock river is

bordered by emergent wetlands dominated by big cordgrass (S. cynosuroid es). The

big cordgrass grows quite dense with high biomass, and heights between 1 and 2 m

(table 1). The dead stalks are left standing over the winter and into the following

spring as new shoots develop (gure 3(d

)). These sites do not change greatly inspecies composition over the seasons, but the ratio of dead to live stalks decreases

over the summer months.

Table 1. List of sites, dominant species and stand parameters. Sites are listed by river: R,Rappahannock; M, Mattaponi; P, Pamunkey; and wetland type: E, emergent; F,forested. NM, not measured.

Average Average stem Stem Clip-plotDominant species vegetation diameter density biomass

Site (April 1997) height (m) (cm) (no. haÕ 1 ) (g haÕ1 )

R1E Spartina cynosuroides 2.36 0.86 528 000 13 468R2E Spartina cynosuroides with 1.26 0.68 552 000 4447

some T ypha spp.R3E Spartina cynosuroides 1.13 0.70 824 000 5472R4E Spartina cynosuroides 1.27 0.50 432 000 3142M1E Acorus calamus 1.35 0.49 920 000 5013M2F Acer rubrum, Fraxinus penn- NM NM NM NM

sylvanica, Nyssa sylvatica

M3E Acorus calamus, Peltandra 1.11 0.66 720 000 4317virginica

M4E Acorus calamus, Peltandra 0.99 0.50 392 000 3984virginica

M5F Acer rubrum, Fraxinus penn- NM NM NM NMsylvanica, Nyssa sylvatica

M6F Quercus phellos, Quercus 24.2 11.05 1675 NMnigra, Platanus occidentalis,Fraxinus pennsylvanica,Nyssa sylvatica

M7F Acer rubrum, Fraxinus 27.2 18.65 710 NMpennsylvanica, Nyssa sylvat-ica, Carya spp., Fagus

grandifoliaP2F Acer rubrum, Fraxinus penn- 15.2 20.95 4780 NM

sylvanica, Nyssa sylvatica,Platanus occidentalis,Pinus taeda

P3F Acer rubrum, Fraxinus penn- 8.1 15.25 2600 NMsylvanica, Nyssa sylvatica,Quercus phellos

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Table 2. Hindcasts of water levels at the test sites. Water level is measured above mean lowerlow water (MLLW).

17 April 1994 9 October 1994Site water level (m) water level (m)

R1E 0.40 0.45R2E 0.40 0.45R3E 0.40 0.45R4E 0.40 0.45M1E 0.93 1.04M2F 0.93 1.04M3E 0.93 1.04M4E 0.93 1.04M5F 0.65 0.80M6F 0.53 0.60

M7F 0.45 0.50P2F 0.60 0.80P3F 0.55 0.55

The forested wetlands of the Mattaponi and Pamunkey rivers are dominated

primarily by red maple (A. rubrum), black gum (Nyssa sylvatica), green ash (Fraxinus

pennsylvanica), willow oak (Quercus phellos) and water oak (Quercus nigra). The

forested sites we chose represent a range of heights, densities and diameters (table 1).Figure 3(e) and ( f ) shows examples of forested wetlands used in this study. Figure 3(e)

shows a medium-density mixed hardwood swamp, while gure 3( f ) is a photo of a

high-density mixed hardwood swamp from the Pamunkey river.

2.3. Water levels

All of the rivers used in this study are tidally inuenced. This was actually

benecial to the project because the Virginia Institute of Marine Science (VIMS)

was able to hindcast water levels for 1994 using formulations representing the

astronomical tides. Sisson and Boon (1998) used observational data collected by theNational Ocean Service and VIMS to calibrate the harmonic analysis and then made

interpolations for our specic site locations. An accuracy assessment found good

agreement between observed and predicted values, with deviations attributable prim-

arily to atmospheric eV ects. Deviations were generally small ( less than 20 cm).

Although VIMS’s hindcasts cannot be used as absolute values of water levels, they

provide a general idea of the degree of ooding at our sites and the stage of the tide

at the time of the space-borne SAR overpasses. The hindcasts were referenced to the

mean lower low water (MLLW) which is used for nautical charts in Virginia waters.

2.4. Field data

Our 1997 eld studies were conducted in the spring and autumn (as the SIR-C

data were collected in spring and autumn) in an attempt to better understand what

state the vegetation may have been in during the 1994 SIR-C overpasses. Field

observations included collection of global positioning system (GPS) locations,

species lists, vegetation diameters, vegetation heights, density counts, biomass for the

emergent sites and water levels at the time of visits.

The sites were located within the images by overlaying GPS locations. Using

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polygons to dene the site boundaries as measured in the eld, these areas were

used as part of the training sets for image classication.

2.5. Vegetation classications

We chose two methods for our automated image classication analysis: a max-

imum likelihood classier (MLC) and a hierarchical classier (HC). We chose theMLC because it is a simple method often used and readily available to potential

users. For comparative purposes we also chose a hierarchical classication which

Hess et al. (1995) found to work quite well in tropical wetlands. The training sets

used for both classiers were chosen from sites we had visited and from the marsh

inventories of the Rappahannock, Pamunkey and Mattaponi rivers (Doumlele 1979,

Silberhorn and Zacherle 1987, Priest et al. 1987, Priest and Dewing 1990).

2.5.1. Maximum likelihood classication

For the maximum likelihood image classication, the 32-bit SIR-C data werescaled and converted to 8 bits, then smoothed using a 7×7 median lter. The data

were passed through this lter 10 times to reduce image speckle. Visual observation

of three band SIR-C image compositions (examples shown in gure 2) demonstrated

that ve classes can be visually separated in the SIR-C imagery: forested wetland,

emergent wetland, agriculture, upland forest and water. Although the human eye is

the best classier, attempts were made to determine the best band combinations for

automated classication that would delineate forested and emergent wetland from

agriculture, upland and water. Since the ‘best’ band combinations diV ered on each

of the image dates and for the two image scenes, we tried a multitude of combinations

in our automated classications. MLCs with all six bands from each date as well as

several 3–4 band combinations from each SIR-C image date were chosen to use in

the classications. For comparison with existing satellites, we chose a C-HH, L-HH,

C-VV combination (i.e. Radarsat, JERS, ERS, although the L-HH of JERS is 35°

incidence angle). We had hoped to compare this with an actual ERS, JERS, Radarsat

combination, but Radarsat data had yet to become available for the Virginia test

site at the time of this analysis. The MLC analyses were conducted in ERDAS

Imagine software. Each dataset combination was classied using a total of 45–50training sets for ve classes (emergent wetland, forested wetland, upland, agriculture

and water).

2.5.2. Hierarchical classication

For the hierarchical classication, the SIR-C data were left unscaled and in

oating point format, so as not to change the radar cross-section. The data were

not converted to dB but left in a linear scale for statistical analyses. This procedure

was conducted in the Grid module of ArcInfo and Splus software (Chambers and

Hastie 1993). Speckle was reduced by applying a 7×7 median lter twice. The same

training polygons which were selected for each of the ve land cover classes for the

MLC were used here. For each class, 500 pixels were randomly selected from the

training polygons for training the model, and another 500 pixels were selected for

testing the model. For each pixel, the response variable, land cover, and the predictor

variables, including each combination of band and polarization, were sampled. These

data were imported into Splus for analysis.

A classication tree model was created for each image date using the tree model

algorithm in Splus. Each model was then evaluated using the test pixels that were

held back for each image date. Since the model algorithm does not limit the size of

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the tree, a certain amount of overtting of the training data occurred. To determine

the optimal size of the tree, the testing data were iteratively run through the model,

with each successive pass reducing the size of the tree by pruning the least important

split from the model. Importance was determined by a cost-complexity measure

which minimized the deviance relative to size (Chambers and Hastie 1993). The

overall deviance of the model was plotted against the size of the tree; the optimalsize of the tree was that with the minimal deviance. This optimal tree model was

then interactively pruned to reduce the complexity of the model while avoiding a

substantial increase in the misclassication error. By reducing the complexity of the

optimal model, the resulting model may be more easily interpreted for underlying

processes.

The classication tree model for each image date was then applied to the original

image data to create a classication image. The output from the model is a decision

tree with each branch being a single split of the data based on a single value of apredictor variable. The split that is selected is the one that maximizes the heterogen-

eity of the response variable for the oV spring datasets. The result is a terminal node

giving an optimum value for the response variable, given the decision path taken

down through the tree. This model was applied to the image by coding each decision

path and running the data through on a pixel-by-pixel basis, assigning class member-

ship. The resulting classied images were then run three times through a majority

lter in ArcInfo, to further reduce speckle. The majority lter uses a moving 3×3

window. Pixel values within that window are replaced with the majority pixel value

if ve out of eight connected pixels within the window have the same pixel value.

2.5.3. Accuracy assessments

Each image classication (MLC and HC) was then tested for accuracy on a

pixel-by-pixel basis using the EPA’s Multi Resolution Land Characteristics (MRLC)

image classication. The MRLC was chosen for comparison because it covers the

entire SIR-C image area and, although it is Landsat TM based, it incorporates

National Wetlands Inventory (NWI) data as well as Digital Terrain Elevation Data

(DTED), population density data and Defense Meteorological Satellite Program

(DMSP) city lights data (www.epa.gov/mrlc). Because it is diYcult to conductstandard attribute accuracy checks on a large area classication, such an assessment

was not made. Instead, comparisons with existing datasets were made including the

National Oceanic and Atmospheric Association’s (NOAA) Coastal Change Analysis

Program (C-CAP) land cover classication for Chesapeake Bay, National High

Altitude Aerial Photography Program (NHAP) colour infrared photos, the 1992

Census for agricultural lands, and United States Geological Survey (USGS) Land

Use Data Analysis (LUDA) program maps (www.epa.gov/mrlc). The EPA found

good agreement between the MRLC and the C-CAP classication, NHAP colour

infrared photos, and the 1992 Census. The MRLC compared less favourably with

the LUDA data, probably due to the coarse scale of the LUDA maps. Since the

MRLC is derived not solely from TM but from a variety of data sources including

NWI, and because it compares favourably with the C-CAP classication, NHAP

photos and 1992 Census data, it is considered a reliable regional scale classication.

Although there are probably errors in the MRLC, using it for our assessment of

accuracy provided a thorough examination of our SIR-C dataset.

For comparative purposes, we conducted one accuracy assessment using training

and testing sites. Traditionally 3–4 sites (minimum) of each class are chosen within

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an image scene which may be used to train the classier, and 3–4 additional sites

for each class are chosen to test the classier. Then the classication image is assessed

based on how accurately the pixels of the testing sites were classied.

While classication errors will be inherently higher by using a pixel-for-pixel

comparison with the MRLC, our assessment is more thorough than that using

training and testing sites.

3. Results

3.1. SIR-C maximum likelihood classication results

The results of each of the band combinations using a MLC are presented in

table 3. For the Rappahannock April scene, combinations of L-HV, C-HH, C-HV

and L-HH, C-HH, C-HV had the best overall accuracies (74%). It is not surprising

that L-HV, C-HH, C-HV had the highest accuracy since it was the three band

combination which appeared to diV

erentiate the ve classes the best. For the Octoberscene, a combination of L-HH, L-HV, C-HV, C-VV had the best overall accuracy

(71%). For other band combinations, diV erentiating agriculture from emergent wet-

land seemed to be causing some trouble. The diV erences in the classications for

April and October scenes were minimal for water, consistently better in April than

October for upland and in most cases better in April for emergent wetland, forested

wetland and agriculture. Most diV erences in these scenes were not due to diV erences

in water levels, as is seen in tables 1 and 2. Di V erences were more likely due to

seasonal changes in the vegetation and crops. The forests are likely to be ushing

out in early April and just beginning senescence in the autumn. While the accuraciesfor emergent wetlands on the Rappahannock were quite high, for forested wetlands

they were marginal. There are very few forested wetlands in the Rappahannock scene

and they are being confused with upland, especially in October when the non-riparian swamplands are drier. It should be noted that these accuracies cannot be

directly compared with those of previous studies (Rignot et al. 1993, Lozano-Garcia

Table 3. Rappahannock MLC classied image accuracies based on MRLC TM classication;

April (A) and October (O).

Emergent ForestedDate/band combination Water wetland wetland Agriculture Upland Total

A-all bands 0.88 0.64 0.29 0.59 0.81 0.73O-all bands 0.52 0.53 0.24 0.46 0.80 0.59A-LHH CHH CVV 0.89 0.63 0.38 0.48 0.78 0.69O-LHH CHH CVV 0.90 0.71 0.37 0.63 0.52 0.66A-LHH CHH CHV 0.89 0.62 0.41 0.62 0.81 0.74O-LHH CHH CHV 0.90 0.38 0.37 0.59 0.49 0.60A-LHH LHV CHV 0.89 0.63 0.45 0.58 0.77 0.72O-LHH LHV CHV 0.89 0.63 0.37 0.56 0.61 0.66A-LVV CHV CVV 0.90 0.35 0.23 0.60 0.65 0.65O-LVV CHV CVV 0.90 0.66 0.49 0.54 0.43 0.60A-LHV CHH CHV 0.90 0.69 0.32 0.65 0.79 0.74O-LHV CHH CHV 0.89 0.57 0.41 0.51 0.65 0.65A-LHH LHV CHV CVV 0.88 0.70 0.33 0.57 0.78 0.72O-LHH LHV CHV CVV 0.89 0.65 0.23 0.69 0.67 0.71A-LHH LHV CHH CHV 0.90 0.64 0.38 0.63 0.76 0.73O-LHH LHV CHH CHV 0.89 0.63 0.36 0.63 0.60 0.66

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and HoV er 1993, Hess et al. 1995, Dobson et al. 1996) because we performed a pixel-

by-pixel comparison with the EPA’s MRLC.

For traditional accuracy assessment and comparison with other studies, we

conducted a training/testing set of sites for the L-HV, C-HH, C-HV MLC classica-

tions of April and October (table 4). The overall accuracies for April and October

using our rigorous approach were 0.74 and 0.65. When the traditional method of training and testing elds was used, the accuracies increased to 0.99 and 0.94,

respectively. Using this traditional approach we also evaluated the MRLC. The SIR-

C accuracies were better than the MRLC for emergent wetlands and agriculture but

not as good as the MRLC for forested wetlands. SIR-C is not as good in October

for mapping forested wetlands because not all of the forests are wet at this time of

year. Since the MRLC was based on summer TM data from 1991–1993 and NWI

data from the 1970s–1990s, we expect some discrepancies. Aside from the seasonal

diV

erences between SIR-C and the TM, there are also likely to be diV

erent tidalconditions. Thus, some error is expected, especially along boundaries of the smaller

rivers and in elds converted to agriculture.

The Mattaponi and Pamunkey river scenes from April and October do not show

as favourable results as the Rappahannock (table 5). In the Mattaponi/Pamunkey

scene there are very few emergent wetlands, and they cover small areas and get

confused with agriculture. The three emergents on the Mattaponi River are sprouting

mud ats in the spring. The big cordgrass sites of the Rappahannock have tall,

Table 4. Accuracy assessment of the Rappahannock MLC L-HV C-HH C-HV classicationsand the MRLC Landsat TM classication using training and testing sites within each image.

Emergent ForestedClassication Water wetland wetland Agriculture Upland Total

MRLC 1.0 0.91 1.0 0.82 1.0 0.98April LHV CHH CHV 1.0 1.0 0.92 1.0 1.0 0.99October LHV CHH CHV 1.0 1.0 0.46 0.91 1.0 0.94

Table 5. Mattaponi/Pamunkey MLC classied image accuracies based on MRLC TM classi-cation; April (A) and October (O). All bands: L-HH, L-HV, L-VV, C-HH, C-HV, C-VV.

Emergent ForestedDate/band combination Water wetland wetland Agriculture Upland Total

A-all bands 0.26 0.13 0.64 0.47 0.72 0.61O-all bands 0.33 0.17 0.47 0.36 0.69 0.54A-LHH CHH CVV 0.39 0.13 0.48 0.55 0.81 0.68O-LHH CHH CVV 0.21 0.18 0.39 0.63 0.71 0.64A-LHH CHH CHV 0.34 0.15 0.37 0.73 0.70 0.67O-LHH CHH CHV 0.29 0.14 0.37 0.62 0.75 0.65A-LVV CHV CVV 0.27 0.13 0.26 0.40 0.80 0.60O-LVV CHV CVV 0.38 0.11 0.34 0.41 0.78 0.60A-LHV CHH CHV 0.36 0.13 0.40 0.66 0.82 0.71O-LHV CHH CHV 0.39 0.15 0.28 0.52 0.80 0.72A-LHH LHV CHV CVV 0.30 0.07 0.38 0.60 0.87 0.72O-LHH LHV CHV CVV 0.38 0.08 0.35 0.44 0.91 0.68A-LHH LHV CHH CHV 0.28 0.10 0.63 0.48 0.87 0.61O-LHH LHV CHH CHV 0.30 0.20 0.45 0.35 0.83 0.59

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vertical stems that are easily diV erentiated from agriculture with the optimal band

combination in spring and autumn. Our Mattaponi/Pamunkey classications per-

formed poorly for emergent wetlands; 20% accuracy at best. We did quite well

classifying upland (69–91%), moderately well classifying agriculture (35–73%) and

forested wetland (26–64%) in most cases. Water was poorly classied (21–39%).

Using L-HV, L-HH, C-HV, C-HH seemed to produce the best results for forestedwetlands (63%) in April and for emergent wetlands in October (20%).

Classied images based on the C-HV, L-HV, C-HH combinations for the

Rappahannock MLC and the L-HV, L-HH, C-HV, C-HH for the Mattaponi/

Pamunkey MLC are presented in gure 4. The Rappahannock April MLC

(gure 4(b)) is comparable with the MRLC (gure 4(a)). However, in October this

band combination overclassied forested and emergent wetlands (gure 4(c)). For

the Mattaponi and Pamunkey river scenes, forested wetlands were overclassied in

April, and agriculture was underclassied (gure 4(e

)) when compared with theMRLC (gure 4(d )). In October, emergents were overclassied and agriculture was

underclassied (gure 4( f )).

3.2. SIR-C hierarchical classication results

The hierarchical classication procedure provided some insight into the utility

of each of the SIR-C bands and allowed us to compare our results with the results

of the tropical SIR-C study by Hess et al. (1995).

Box plots from each SIR-C scene from each image date are presented in gures

5–8. The white line in the middle of the box represents the median, and the upperand lower quartile are shown with the box limits. It is evident from these plots that

L-HV was consistently the best band for diV erentiation of woody (fwet and up)

versus herbaceous (ag and ewet) vegetation. This is consistent with the ndings of Hess et al. (1995). L-HH was the most useful band for diV erentiating forested wetland

from upland forest in all cases except the October Rappahannock image. This is

probably because most of the forested wetland sites were not ooded in October on

the Rappahannock. Therefore, diV erentiation between forested sites was low. Hess

et al. (1995) and Wang et al. (1995) also found L-HH to be the best band for

separating ooded from non-ooded forests. In this study we found C-HH, C-HVand in some cases C-VV (gure 5) to be useful in diV erentiating emergent wetland

from agriculture. Hess et al. (1995) found C-HH to be the most useful band for

pasture versus oating meadow diV erentiation. Some of the variation between our

study and Hess’s may be due to the diV erent vegetation types and also due to the

diV erent incidence angles. Our incidence angle was 25°, and Hess’s were 30–35°.

Using a theoretical model, Wang et al. (1995) found HH to be better than VV for

diV erentiating ooded from non-ooded forests, and they also found small incidence

angles to be better than large incidence angles. This is because there is better canopy

penetration at a steeper incidence angle. Overall, our ndings are consistent with

the ndings of Hess et al. (1995) and Wang et al. (1995).

An example of a classication decision tree is presented in gure 9 to illustrate

the hierarchical classication procedure. This is the decision tree developed for the

April Rappahannock image after pruning for simplication. At each fork in the tree

a single backscatter value associated with a single frequency/polarization combina-

tion is used to divide the data. Note that there is more than one terminal node for

some of the vegetation classes. Although only two channels of SAR data (L-HV and

C-HH) were used for the April Rappahannock classication, more channels were

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F i g u r e 4 .

( a ) E P A ’ s M R L C

L a n d s a t T M - b a s e d c l a s s i c a t i o n o f t h e R a p p a h a n n o c k R i v e r c o m p a r e d w i t h t h e

S I R - C m a x i m u m l i k e l i h o o d

c l a s s i c a t i o n

i m a g e s f r o m

( b ) A p

r i l a n d ( c ) O c t o b e r 1 9 9 4 .

( d ) E P A ’ s M R L C

L a n d s a t

T M - b a s e d c l a s s i c a t i o n o

f t h e M a t t a p o n i a n d P a m

u n k e y R i v e r s

c o m p a r e d w i t h t h e S I R - C m a x i m u m l i k e l i h o o d

c l a s s i c a t i o n i m a g e s f r o m

( e ) A p r i l a n d ( f ) O c t o b e r 1

9 9 4 .

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F i g u r e 5 .

B o x p l o t s o f S I R - C C - H H ,

C - H V ,

C - V V ,

L - H H ,

L - H V a n d L - V V S I

R - C b a c k s c a t t e r f r o m A p r i l 1 9 9 4 R a p p a h a n n o c k R i v e r t r a i n i n g p i x e l s

o f a g r i c u l t u r e ( a g ) , e m e r g e n

t w e t l a n d s ( e w e t ) , f o r e s t e d w e t l a n d s ( f w e t ) , u p l a n d ( u p ) a n d w a t e r ( w a t ) .

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F i g u r e 6 .

B o x p l o t s o f S

I R - C C - H H ,

C - H V ,

C - V V

, L - H H ,

L - H V a n d L - V V

S I R - C b a c k s c a t t e r f r o m O c t o b e r 1 9 9 4 R a p p a h a n n o c k R i v e r t r a i n i n g

p i x e l s o f a g r i c u l t u r e ( a g ) , e m e r g e n t w e t l a n d s ( e w e t ) , f o r e s t e d w e t l a n d s ( f w e t ) , u p l a n d ( u p ) a n d w a t e r ( w a t ) .

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F i g u r e 7 .

B o x p l o t s o f S I R - C C - H H ,

C - H V ,

C - V V , L - H H ,

L - H V a n d L - V V S I R - C b a c k s c a t t e r f r o m A p r i l

1 9 9 4 M a t t a p o n i / P a m u n k e y

R i v e r s t r a i n i n g

p i x e l s o f a g r i c u l t u r e ( a g ) , e m e r g e n t w e t l a n d s ( e w e t ) , f o r e s t e d w e t l a n d s ( f w e t ) , u p l a n d ( u p ) a n d w a t e r ( w a t ) .

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F i g u r e 8 .

B o x p l o t s o f S

I R - C C - H H ,

C - H V ,

C - V V

, L - H H ,

L - H V a n d L - V V

S I R - C b a c k s c a t t e r f r o m O

c t o b e r 1 9 9 4 M a t t a p o n i / P a m u n k e y R i v e r s

t r a i n i n g

p i x e l s o f a g r i c u l t u r e ( a g ) , e

m e r g e n t w e t l a n d s ( e w e t ) , f o

r e s t e d w e t l a n d s ( f w e t ) , u p l a n d ( u p ) a n d w a t e r ( w a t ) .

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Figure 9. Decision tree for the April 1994 Rappahannock River SIR-C classication. ag,agriculture; up, upland; wat, water; fwet, forested wetlands; and ewet, emergentwetlands.

necessary ( 3–5 ) for the October Rappahannock classication and the two Mattaponi/

Pamunkey classications. While the hierarchical classication theoretically would

seem to be a better method for image classication (table 6), we found it comparable

to using 3 or 4 band combinations with a MLC (tables 3 and 5). The overall

accuracies were comparable with the best MLC band combinations. However, it is

interesting that the HC accuracies for emergent and forested wetland were better inOctober than April on the Rappahannock (table 6). April was nearly always better

for mapping wetlands using the MLC. Using the HC technique on the Mattaponi /

Pamunkey river scenes resulted in better discrimination of emergents but poorer

discrimination of forested wetlands than using the MLC method (table 3).

Classied images derived from the decision trees of the HC are presented in

gure 10. The April Rappahannock classication map (gure 10(b)) is comparable

to the MRLC (gure 10(a)). It appears as though the HC is overclassifying forested

Table 6. Hierarchical classied image accuracies based on MRLC TM classication.

Emergent ForestedDate/scene Water wetland wetland Agriculture Upland Total

April Rappahannock 0.92 0.58 0.39 0.67 0.81 0.75October Rappahannock 0.92 0.70 0.52 0.56 0.47 0.62April Mattaponi/Pamunkey 0.28 0.43 0.30 0.45 0.85 0.65October Mattaponi/ 0.30 0.22 0.32 0.51 0.81 0.64

Pamunkey

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F i g u r e 1 0 .

( a ) E P A ’ s M R L C L a n d s a t T M - b a s e d c l a s s i c a t i o n o f t h e R a p p a h a n n o

c k R i v e r c o m p a r e d w i t h t h e S I R - C h i e r a r c h i c a l c l a s s i

c a t i o n i m a g e s

f r o m ( b ) A p r i l a n d ( c

) O c t o b e r 1 9 9 4 .

( d ) E P A ’ s M

R L C L a n d s a t T M - b a s e d c

l a s s i c a t i o n o f t h e M a t t a p o

n i a n d P a m u n k e y R i v e r s c o m p a r e d w i t h

t h e S I R - C h i e r a r c h i c a l c l a s s i c a t i o n i m a g e s f r o m ( e ) A p r i l a n d ( f ) O c t o b e r 1 9 9 4 .

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and emergent wetlands in the October Rappahannock scene (gure 10(c)); this is

also true of the MLC (gure 4(c)). For the Mattaponi/Pamunkey HC (gure 10(e)

and ( f )), forested wetland classication is comparable to the MRLC (gure 10(d ))

with a slight underclassication. However, the emergents are overclassied, and

agriculture is underclassied. This is similar to the October MLC (gures 4(e)

and ( f )).

4. Discussion and conclusions

This investigation has demonstrated that multi-band SAR data can be used to

map wetlands with reasonable accuracy. The two methods used resulted in compar-

able products. Since the MLC is the simpler approach and is available to a wide

array of potential users, it would be the method of choice. The keys are: (1) using

the combination of bands which optimizes the discrimination of vegetation types;(2) reducing image speckle; and (3) collection of SAR data during a ooded time.

L-HV was determined to be the best band for discriminating woody from herbaceous

vegetation, L-HH was the best band for discriminating forested wetland from upland

forest, and C-HH was best for discriminating emergent wetlands from agriculture.

We found existing satellite frequency/polarizations (Radarsat, ERS, JERS) may be

used to produce quite accurate maps (65%). The advantage of using SAR over

visible data is the detection of forested wetlands. It is very diYcult to detect ooding

beneath a forested canopy without SAR. The best method for vegetation mapping

would probably be a synergistic approach between visible and SAR data. Lozano-Garcia and HoV er (1993) found that a combination of TM and SIR-B SAR data

signicantly improved their forest classication. A synergistic approach would be

likely to greatly improve our classication accuracies and allow for more detailed

class levels.

The results of this study show the need for multi-polarization/multi-frequency

SAR data for a complete wetland mapping and monitoring capability. HH polariza-

tion was found to be most useful for detecting wetlands. While L-band was found

to be best for discriminating ooded from non-ooded forests, C-band was best fordiscriminating emergent wetlands from agriculture and herbaceous uplands. Finally,

as was found with the hierarchical classication, the cross-polarizations (HV) are

necessary for discrimination of woody versus herbaceous vegetation types.

Acknowledgments

This research was funded by EPA award number CR 823077-01- 0 to ERIM.

Although this research was supported by EPA, it has not been subject to agency

review and therefore does not necessarily reect the view of the agency, and nooYcial endorsement should be inferred. The ERS data used in this study were

provided by the European Space Agency under Experiment A02.USA133. The SIR-

C data were provided by NASA. The authors would like to express their appreciation

to all the landowners of the sites used in this study for allowing us to access their

property: Frank Parker, Thomas Tullidge, Blandeld Association, John Ariail and

Jane RuYn. The authors would like to especially thank Billy Mills for assisting us

in locating and contacting landowners. Many thanks to Jim Slawski for his assistance

with the SIR-C data and Mark Tukman for his assistance in the eld.

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Analysis of Space Borne SAR Data for Wetland Mapping in Virginia Riparian Ecosystems