extending satellite remote sensing to local scales: land

www.elsevier.com/locate/rseRemote Sensing of Environment 88 (2003) 144–156

Extending satellite remote sensing to local scales: land and water resource

monitoring using high-resolution imagery

Kali E. Sawaya*, Leif G. Olmanson, Nathan J. Heinert, Patrick L. Brezonik, Marvin E. Bauer

Department of Forest Resources, University of Minnesota, 1530 N. Cleveland Avenue St. Paul, MN 55108-6112, USA

Received 22 May 2002; received in revised form 19 March 2003; accepted 24 April 2003

Abstract

The potential of high-resolution IKONOS and QuickBird satellite imagery for mapping and analysis of land and water resources at local

scales in Minnesota is assessed in a series of three applications. The applications and accuracies evaluated include: (1) classification of lake

water clarity (r2 = 0.89), (2) mapping of urban impervious surface area (r2 = 0.98), and (3) aquatic vegetation surveys of emergent and

submergent plant groups (80% accuracy). There were several notable findings from these applications. For example, modeling and estimation

approaches developed for Landsat TM data for continuous variables such as lake water clarity and impervious surface area can be applied to

high-resolution satellite data. The rapid delivery of spatial data can be coupled with current GPS and field computer technologies to bring the

imagery into the field for cover type validation. We also found several limitations in working with this data type. For example, shadows can

influence feature classification and their effects need to be evaluated. Nevertheless, high-resolution satellite data has excellent potential to

extend satellite remote sensing beyond what has been possible with aerial photography and Landsat data, and should be of interest to resource

managers as a way to create timely and reliable assessments of land and water resources at a local scale.

D 2003 Elsevier Inc. All rights reserved.

Keywords: IKONOS; Remote sensing; High resolution imagery; Lake clarity; Aquatic vegetation; Impervious surface

1. Introduction

Although high-resolution imagery in the form of aerial

photography has been available for many years, the launch of

the IKONOS-2 by Space Imaging in September 1999 has

signaled a new era in satellite remote sensing. With multi-

spectral digital imagery approaching that of small to medium

scale photography, we are in a new period of applications

development. Our objective has been to evaluate high-reso-

lution satellite imagery in a variety of applications involving

monitoring of land and water resources. We approach each of

three applications at a ‘‘local’’ scale and address a pertinent

aspect of water quality in Minnesota. Each application

utilizes a single-date scene of IKONOS or QuickBird imag-

ery covering approximately 11�11 km areas.

To demonstrate the potential of the imagery, these

applications each take a closer look at digitally classifying

a particular aspect of monitoring and mapping land and

0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2003.04.0006

* Corresponding author. Tel.: +1-612-624-2202; fax: +1-612-625-

5212.

E-mail address: [email protected] (K. Sawaya).

water resources. We include: (1) the classification of lake

water clarity, (2) the determination of urban impervious

surface area, and (3) aquatic vegetation surveys of emer-

gent and submergent plant groups. Each application

attempts to take advantage of the increased spatial resolu-

tion and the multispectral properties of the data to digitally

classify aspects of the environment that have not been

possible to this degree of detail with satellite imagery in

the past. The first two applications take advantage of

existing methodologies for moderate resolution Landsat

data that have been modified for use with high-resolution

satellite data. These applications seem to make a smooth

transition to the new imagery type and should provide an

‘‘out of the box’’ solution to resource managers.

Throughout the paper we address several considerations

common to applications development with high-resolution

satellite imagery including:

1. Imagery acquisition parameters. Appropriate acquisition

windows for each type of application are identified and

the challenges inherent in working with large geographic

extents of high-resolution satellite data are discussed.

K. Sawaya et al. / Remote Sensing of Environment 88 (2003) 144–156 145

2. Field data sampling. Considerations for working with

point and polygon field data, preferred characteristics of

field reference data for use with high-resolution data, and

alternative approaches when ideal characteristics are not

available are discussed.

3. Spectral confusion. Instances of spectral confusion

associated with cover type classifications and how we

have amended our methods to account for them are

described.

4. Shadows. The effects of shadows on image classification

and ways these errors can be accounted for or avoided are

discussed.

5. Accuracy assessments. Approaches to modeling and

assessing the accuracy of output maps using various field

data sets, Landsat data of a similar date, and aerial photo

interpretation are discussed.

The paper is organized into three individual sections,

each reporting on a separate application and approach. A

concluding section revisits and synthesizes the above

considerations.

2. Lake water clarity classification

2.1. Background

Lakes are important recreational and aesthetic resources

that add to economic stability and quality of life. Protecting

and monitoring lake water quality is a major concern for

many local and state agencies. However, because of expense

and time requirements for ground-based monitoring, it is

impractical to monitor more than a small fraction of lakes by

conventional field methods. High-resolution satellite remote

sensing is another tool that can potentially be applied to

gather information needed for water clarity assessments in

lake-rich areas like Minnesota.

Results from several studies (e.g., Kloiber, Brezonik, &

Bauer, 2002; Kloiber, Brezonik, Olmanson, & Bauer, 2002;

Lillesand, Johnson, Deuell, Lindstrom, & Meisner, 1983)

have demonstrated a strong relationship between Landsat

Multispectral Scanner (MSS) or Thematic Mapper (TM)

data and ground observations of water clarity and chloro-

phyll a. In Minnesota, Olmanson, Bauer, and Brezonik

(2002) have developed a water clarity image processing

methodology and completed statewide assessments of over

10,500 lakes for thef 1990 and f 2000 time periods using

Landsat imagery. In this statewide assessment, water clarity

was evaluated for lakes over 8 ha (20 acres) in size, while

the smaller lakes and ponds were excluded due to the

relatively low spatial resolution of the Landsat imagery.

IKONOS imagery has four multispectral bands similar to

Landsat TM bands 1–4 and high spatial resolution, making

it a good candidate for applying previous methods to the

assessment of smaller lakes and ponds. In this study, we

used a September 4, 2001 IKONOS high-resolution satellite

image to assess the water clarity of smaller lakes and ponds

for a city scale analysis of water quality. These results are

compared to a lake water clarity classification using an

August 30, 2001 Landsat TM image. Assessment of smaller

lakes and ponds is important since they tend to be more

susceptible to impacts than larger lakes.

The overall objective of our research was to estimate

variables related to key management indicators, such as the

trophic state indices of Carlson (1977). The three common

water quality variables that indicate lake trophic state are

total phosphorus (TP), chlorophyll a (chla), and Secchi disk

transparency (SDT). Lake management agencies and organ-

izations use these variables for measurements, along with

various transformations such as the trophic state indices

(TSI). SDT is the most consistently collected trophic state

indicator, and it is strongly correlated with the responses in

the blue and red bands of Landsat TM/ETM+ data (Kloiber,

Brezonik, & Bauer, 2002; Kloiber, Brezonik, Olmanson et

al., 2002). Therefore, most of our research to date has

involved calibrating Landsat TM data with ground-based

SDT measurements and estimating SDT for all lakes in an

image from the regression equation developed in the cali-

bration step. The results then can be mapped directly as

distributions of SDT in the lakes, or the estimated SDT can

be converted to Carlson’s trophic state index based on

transparency: TSI(SDT) = 60–14.41 ln(SDT).

It is important to recognize that other factors besides

algal turbidity (as indicated by chlorophyll levels) may

affect SDT in lakes. Most important of these (non-trophic-

state) factors are humic color and non-algal turbidity (in-

cluding soil-derived clays and suspended sediment). For this

reason, we report our results based on SDT calibrations as

satellite-estimated SDT or TSI(SDT), which clearly identi-

fies the value as an index based on transparency, rather than

the generic term, TSI.

The specific objective for this study was to perform an

assessment of TSI(SDT) for the City of Eagan, MN. This

area was particularly well suited for this study since it has

375 small lakes, ponds, and wetlands and a well-established

lake monitoring program.

2.2. Methods

We used methods developed by Olmanson (1997) and

continued in subsequent studies (e.g., Kloiber, Brezonik, &

Bauer, 2002; Kloiber, Brezonik, Olmanson et al., 2002;

Olmanson et al., 2002) to apply Landsat imagery to regional

scale assessments of lake water clarity. We made minor

modifications including the addition of a lake polygon layer

to minimize spectral confusion between open water, shad-

ows and asphalt features to make these methods compatible

with high-resolution satellite imagery.

2.2.1. Satellite imagery and lake reference data

Images for water clarity assessment were selected from a

late summer index period (July 15–September 15, with a

K. Sawaya et al. / Remote Sensing of Environment 88 (2003) 144–156146

preference for August). This period was found to be the best

index period for remote sensing of water clarity in Minne-

sota (Kloiber, Brezonik, Olmanson et al., 2002). There are at

least two major advantages to using images from this

period: (1) short-term variability in lake water clarity is at

a seasonal minimum, and (2) most lakes have their mini-

mum water clarity during this period. In addition, it is

preferable to have images from near anniversary dates for

change detection.

We acquired two satellite images, August 30, 2001

Landsat TM (path 27, rows 29 and 30) and September 4,

2001 IKONOS of Eagan, MN for this assessment. Both

images were of high quality and free of haze, clouds and sun

glint. It is critical to avoid IKONOS images displaying

specular reflection, or ‘‘sun glint’’ effects from lakes for this

application. Certain combinations of IKONOS view azi-

muths and zenith angles can result in bidirectional reflec-

tance that saturates the sensor, making the data unusable for

this and other water related applications. Although the

imagery used in this analysis was suitable, specular reflec-

tance effects have created problems for some of the other

images we acquired for lake water clarity assessments

between 1999 and 2001. We did not perform atmospheric

correction or normalization of the imagery for the regression

method used.

The availability of lake reference data was excellent due

to the City of Eagan’s Water Resources program and

volunteer participation in the Citizen Lake Monitoring

Program. The programs provided 94 SDT lake reference

points for the Landsat image taken within three days of the

image acquisition and 13 SDT lake reference points for the

IKONOS image taken within seven days of the image

acquisition. The SDT data were distributed over a wide

range of water quality.

2.2.2. Classification procedures

This section summarizes our image classification proce-

dures; more detail is provided by Olmanson et al. (2002),

and the rationale for the procedures is described by Kloiber,

Brezonik, Olmanson et al. (2002). We used ERDAS Imag-

ine image processing software, and ArcView geographical

information system (GIS) software, for the image processing

steps. Acquiring a representative image sample from each

lake used for calibration or accuracy assessment was our

primary objective. Ideally, the sample should represent the

center portion of the lake in at least five meters of water (or

twice the SDT measurement) where reflectance from veg-

etation, the shoreline, or the lake bottom do not affect the

spectral response. It was also critical to avoid shadowed

portions of lakes, which would lead to unnaturally clear lake

estimations.

We produced a ‘‘water-only’’ image by performing an

unsupervised classification in ERDAS Imagine. Because

water features tend to have very different spectral charac-

teristics from terrestrial features, water was put into one or

more distinct classes that we could easily identify. We then

masked out terrestrial features creating a water-only image.

This method worked well for the Landsat imagery; how-

ever, with IKONOS imagery this clustering also included

pixels from shadows, asphalt, and other dark features.

Therefore, we created a lake polygon layer to help mask

the non-water dark features on the IKONOS image. Then

we performed a second unsupervised classification on the

water-only image and generated spectral signatures of each

class. We used these signatures, along with the location

where the pixels occur, to differentiate classes containing

clear water, turbid water, and shallow water (where sedi-

ment and/or macrophytes affect spectral response). Based

on this analysis, we recoded classes to avoid shadow,

vegetation, bottom, and terrestrial effects when selecting

lake sample locations. Digital number values from the

imagery were obtained to develop relationships with mea-

sured SDT. For this assessment, we used a polygon layer,

described in Olmanson et al. (2002), to help automate the

process. We used the signature editor in ERDAS Imagine

to extract the spectral data from the image for each sample

location.

We calibrated the Landsat image using the 94 SDT

measurements collected within three days of the image

acquisition date. Using log-transformed SDT data as the

dependent variable and Landsat Thematic Mapper band 1

(TM1) and the TM1/TM3 ratio as independent variables, we

performed a multiple regression. The regression model, with

r2 = 0.76 and SEE = 0.39, for prediction of SDT from the

Landsat TM data was:

lnðSDTÞ ¼ 1:493*ðTM1:TM3Þ � 0:035ðTM1Þ � 1:956 ð1Þ

Once we developed the model for the Landsat image, we

used another polygon layer with all of Eagan’s lakes, ponds,

and wetlands 0.08 ha and larger to extract data from all

possible water-bodies with enough unaffected pixels to

predict water clarity. Forty-eight of Eagan’s lakes and ponds

had a sample of at least eight unaffected pixels that we used

to predict water clarity. We used the model above, devel-

oped from the entire Landsat image, to predict water clarity

for 48 of Eagan’s lakes and ponds. For this assessment, the

smallest pond that we were able to predict water clarity for

using the Landsat TM image was 1.5 ha.

We then calibrated the IKONOS image using two differ-

ent datasets. The first dataset was 13 SDT measurements

collected within seven days of the image acquisition date.

The other dataset was 48 water clarity measurements

extracted from the Landsat image. We used this dataset to

compare the compatibility of the IKONOS imagery with

Landsat TM imagery and to explore an optional calibration

method if sufficient ground observations were not available

for a given scene. We performed a multiple regression using

log-transformed SDT data as the dependent variable and

IKONOS band 1 (IK1) and the IK1:IK3 ratio as independent

variables.


Lake water clarity maps can then be created from the

regression model by two methods. The first method

applies the model to each individual water pixel. This

method creates a pixel-level lake map. With this map, all

water pixels are classified and intra-lake variability can be

evaluated. The second method uses the digital numbers

collected from the sample of each lake to calculate an

average water clarity estimate. The data can then be

linked to a lake polygon layer to create a lake-level water

clarity map. This latter method has the advantage of

generating a single water clarity number for each lake

that can be used in other analyses or included in a water

clarity database.

2.3. Results and discussion

This assessment showed strong relationships between

both water clarity datasets (TM-derived and SDT-derived)

and the spectral-radiometric response of the IKONOS

data. The regression model (n = 48, r2 = 0.94, SEE = 0.15)

for prediction of SDT from the IKONOS data using the

water clarity data derived from the Landsat TM image

was:

lnðSDTÞ ¼ 1:958*ðIK1:IK3Þ � 0:004ðIK1Þ � 2:957 ð2Þ

The regression model (n = 13, r2 = 0.89, SEE = 0.22) for

prediction of SDT from the IKONOS data using the

available SDT data (Fig. 1) was:

lnðSDTÞ ¼ 1:832*ðIK1:IK3Þ � 0:004ðIK1Þ � 2:813 ð3Þ

The very strong relationship of r2 = 0.94 using the

Landsat-derived SDT to calibrate the IKONOS image

indicates that the two types of imagery are compatible

Fig. 1. Comparison of IKONOS and field meas

and have similar spectral-radiometric responses. The strong

relationship between SDT and spectral-radiometric re-

sponse of the IKONOS imagery that are similar to results

we have seen with Landsat imagery indicates that IKO-

NOS imagery can be used to assess the water clarity of

smaller lakes and ponds. The similarity in the resulting

models also indicates that Landsat imagery can be used to

calibrate IKONOS images that do not have sufficient field

reference data. A comparison of the resulting TSI(SDT)

values calculated from the Landsat image and from the

IKONOS image using each model for 48 lakes and ponds

both indicated a very strong agreement with an r2 = 0.95

(Fig. 2).

The 4-m resolution of the IKONOS imagery allowed us

to assess smaller water bodies than is possible with

Landsat 30-m resolution imagery. We easily assessed

water bodies with a minimum size of 0.08 ha included

in the lake, pond, and wetland polygon layer when open

water conditions and unaffected pixels existed. We used

347 lake, pond, and wetland polygons 0.2 ha and larger

on the Eagan lake polygon layer to extract the spectral-

radiometric data from the imagery. Of those lakes and

ponds, 236 had a sample of at least eight unaffected pixels

used to predict water clarity. In contrast, we could only

estimate and map the water clarity of 48 lakes and ponds

in Eagan when using Landsat imagery, as the minimum

lake size assessed with Landsat 30-m resolution imagery

was 1.5 ha.

High-resolution satellite data were also useful for gen-

eral analysis of city land use and land cover features. Visual

assessment of how land use/cover affects water clarity can

be investigated by overlaying the classified lakes on the

original IKONOS imagery. Fig. 3 shows some of the

different land use/cover features and corresponding effects

on water clarity. For example, in the southeastern corner of

Eagan, the Lebanon Hills Regional Park is an area with an

urements of lake water clarity, TSI(SDT).

Fig. 2. Comparison of Landsat and IKONOS estimates based on Landsat modeling of lake water clarity, TSI(SDT).


abundance of forest and wetland areas and limited devel-

opment in the form of parking lots and pavilions for park

visitors. This area has relatively high lake water clarity with

TSI(SDT) of around 50 and SDT of f 2 m. In many of the

residential and commercially developed areas, stormwater

is directed into lakes using them as convenient reservoirs.

The increase in impervious surface area and direct connec-

tion to the stormwater system has dramatically changed the

hydrology of many water bodies in Eagan. These changes

Fig. 3. Lake water clarity classification of IKONOS multispectral data ove

of increased watershed size, amount of runoff and quality

of runoff water have impacted many of Eagan’s water

bodies. These impacts can be seen in Fig. 3 where water

clarity of lakes and ponds in many of the developed areas is

generally TSI(SDT) 65–70 and SDT of f 0.5 m due to

eutrophication.

This study showed the usefulness of high-resolution

satellite imagery for water clarity assessments at a city

scale. This type of information can be very useful in city

rlaid on panchromatic land image. (Imageryn Space Imaging L.P.).


planning and lake management to help monitor and protect

water clarity at a local scale.

3. Impervious surface mapping

3.1. Background

Impervious surfaces, or areas impenetrable by water,

negatively affect the natural environment. These built envi-

ronments—including roads, rooftops, sidewalks and parking

lots—increase the rate of stormwater runoff, which transport

surface pollutants to receiving lakes and ponds. Thus,

impervious surfaces threaten water quality (Barnes, Morgan,

& Roberge, 2000; Sleavin, Civco, Prisloe, & Giannatti,

2000). In addition, Estes, Gorsevski, Russel, Quattrochi,

and Luvall (1999) suggest that impervious surfaces are

related to energy balances, urban heat island effects, habitat

fragmentation, and poor landscape aesthetics. To monitor

these impacts, resource managers have quantified the de-

gree, extent, and spatial distribution of impervious surface

areas using various methods including ground surveys,

aerial photography interpretation, and satellite remote sens-

ing. Satellite imagery, particularly Landsat TM and ETM+,

has recently emerged as an approach with the capability to

effectively estimate the percentage of impervious cover in

urban areas (Bauer, Doyle, & Heinert, 2002; Civco & Hurd,

1997; Ridd, 1995).

Previous research using Landsat TM data acquired in

1986, 1991, 1998, and 2000 across the Twin Cities Metro-

politan Area suggests that a strong relationship (correlation

f 0.9) exists between the Landsat spectral-radiometric

response and percent impervious surface area calculated

from measurements of digital orthophoto quadrangle

(DOQs) imagery (Bauer et al., 2002). In this study, we

Fig. 4. Sample areas delineated over Digital Orthophoto Quadrangles (DOQs)

calibration and evaluation.

investigate whether a similar method is suitable for high-

resolution IKONOS imagery at a local scale for the City of

Eagan, MN. Eagan is located southwest of St. Paul, MN and

covers 89 km2. In the 1990s, Eagan’s population growth

increased 33%, reaching 63,557 in 2000. The landscape

consists mostly of single/multi-family residential, commer-

cial/industrial, parks and recreational areas, vacant and

agricultural lands, and numerous small lakes and ponds.

3.2. Methods

We performed several background tests to determine

which spectral transformation provided the strongest rela-

tionship to percent impervious surface area. For IKONOS

data, a correlation of 0.90–0.95 suggested that the normal-

ized difference vegetation index (NDVI) provided the best

relationship. Other transformations considered were IR/red

ratio and principal components. The ratio had a correlation

of 0.93 and the second principal component had a correla-

tion of 0.86 with percent impervious. The ratio and the

NDVI had similar results, but we selected NDVI due to its

familiarity. In past research with Landsat TM imagery, we

used tasseled cap greenness (Bauer et al., 2002), but this

was not possible since the coefficients for a tasseled cap

transformation of IKONOS data have not been determined.

IKONOS digital imagery of the study area was acquired

on September 4, 2001. The imagery was clear and cloud-

free; therefore, no atmospheric correction was necessary.

Using 270 ground control points, we rectified and registered

the imagery to DOQs covering the same area, with an RMS

error of 1.24 m.

We began our image processing by performing a general

land cover classification using a supervised, maximum

likelihood approach in ERDAS Imagine. Due to spectral

confusion among some water features and asphalt, we hand

used to calculate varying degrees of impervious surface area for model


digitized open water and wetland areas using heads up

digitization from the IKONOS imagery. Due to spectral

confusion between bare areas and concrete, we also hand

digitized bare fields and extraction areas. Using these

techniques, we were able to achieve an overall classification

accuracy of 93% for urban, vegetation, bare areas, and

water. Pixels from the urban class were retained for the

determination of percent imperviousness. Vegetation and

bare classes were recoded as 0% impervious.

Using DOQs, we then selected well distributed samples

across the study area to represent varying degrees of

imperviousness. The objective was to obtain a complete

range of percent imperviousness with at least two samples

for each 10% interval. Sample areas ranged in size from 300

to 2400 pixels. We used ArcView 3.2 to digitize the

boundaries around each impervious feature and calculate

the impervious surface percentage for that individual sample

area (Fig. 4).

After calculating and attributing the percent impervious-

ness for each sample area, we determined the mean NDVI

value for each sample area by overlaying the sample onto

the NDVI transformed IKONOS image. We then created a

second order polynomial regression equation with the mean

NDVI value as the independent variable and percent imper-

vious surface area as the dependent variable. With this

regression equation, we estimated impervious surface per-

cent for each image pixel and rescaled the output values

from 0% to 100%. To evaluate classification accuracy, we

compared 25 additional independent samples of impervious

surface area determined from the DOQs to the IKONOS

map of imperviousness.

4. Results and discussion

The results of the regression analysis showed a very

strong relationship (r2 = 0.95) between the NDVI and per-

Fig. 5. Relationship of percent impervious surface are

cent imperviousness (Fig. 5). The resulting polynomial

equation used to estimate percent imperviousness across

the image was:

Y ¼ 44:339X 2 � 196:39X þ 95:645 ð4Þ

The agreement between IKONOS estimates and meas-

urements from the additional sample areas of DOQs was

very high with a linear correlation of 0.98 (Fig. 6). Fig. 7

displays a map of all impervious surfaces in the City of

Eagan as a continuous gray scale of 0–100% impervious

surface area. Note the degree of impervious detail that is

possible to discern in the built environment including bike

trails, vegetation along road medians, and individual

driveways. Resource managers can now recode the 0–

100% scale to appropriate indices for their individual

analysis.

The land cover classification step helped to stratify and

improve the accuracy of the final map. Separating water,

vegetation, and bare areas helped prevent mapping several

pervious features as impervious. Without masking, open

water features are erroneously classified as 80–100%

impervious due to their low NDVI. The presence of tree

canopy shadows and bare patches in lawns can also

introduce a small amount of error into the impervious

surface estimations. For future land cover classifications

of high-resolution imagery, we anticipate using object-

based classification methods to minimize some of these

effects.

Shadows from buildings and other tall features affected

the overall classification of impervious surface in high-

resolution satellite imagery. They tended to be most prob-

lematic around the perimeter of forested and urban areas.

Shadows have low NDVI values, thus giving a false

measure of impervious features in certain areas. It is difficult

to ascertain what features are within a shadowed pixel using

digital classification. Therefore, manual image interpretation

a determined from DOQs and IKONOS NDVI.

Fig. 6. Comparison of measured and IKONOS estimates of percent impervious surface area.


may be necessary if it is crucial to know the composition of

shadowed pixels. The high radiometric resolution option

(11-bit for IKONOS) may also provide a means to resolve

and characterize ground features within shadowed areas and

will be evaluated in future studies. Though the presence of

shadows can affect accuracy in this application, they tend to

affect individual pixels or small groups of pixels. Most

environmental models would analyze numerous pixels to-

gether (watershed, municipality, etc.), minimizing this error

across the landscape.

The September 4, 2001 imagery represented a time of

leaf-on conditions where tree crowns and other vegetation

could potentially obscure some impervious features. Our

Fig. 7. Maps of percent impervious surface area deriv

work with Landsat data indicates that these conditions

introduce a statistically insignificant amount of error. We

have recently acquired leaf-off IKONOS imagery (Novem-

ber 2002) to determine differences or advantages between

using leaf-on, leaf-off, or a combination of the two with

high resolution satellite imagery.

We believe this approach to mapping impervious surface

is superior to assigning an assumed impervious value to a

land cover type of an existing map. We suggest that a pixel-

by-pixel approach is more accurate and better represents the

gradation of impervious values across features in neighbor-

hoods and industrial areas. This application takes advantage

of the increased spatial resolution of the high-resolution

ed from classification of IKONOS NDVI data.


satellite imagery and should be of interest to natural

resource and city managers who need to evaluate storm-

water runoff, heat island effects, the protection of green

corridors, and other types of assessments quantifying built

environments.

5. Aquatic vegetation surveys

5.1. Background

While lakes are well known for their recreational and

aesthetic value, traditionally, society has considered wet-

lands as nuisances and problems that need to be cleaned

up. Statewide, Minnesota has drained over half of the

original wetlands (around 4.5 million hectares) for agricul-

tural and development purposes, and many of the remain-

ing wetlands are degraded. A frequent cause of wetland

degradation is increased storm water discharge resulting

from changes such as increases in impervious surface area

or installation of storm water systems in urban and subur-

ban areas, as well as tiling and ditching systems in

agricultural areas. Changes in hydrology affect the water

quality and quantity, and may severely impact the function

of wetlands (Daily, 1997). When too many wetland plants

are removed or impacted, water quality, wildlife, and fish

populations can suffer (Mitsch & Gosselink, 1993). These

plants are important because they help protect water

quality, provide habitat for fish and wildlife, and provide

economic and aesthetic opportunities (Barbier, Burgess, &

Folke, 1994).

Aquatic plants in lakes and wetlands are beginning to

be recognized as important ecosystem features in need of

protection. As a result of this greater appreciation for

aquatic plants in wetland and lake environments, aquatic

plant surveys and assessments are becoming part of routine

monitoring efforts conducted by consultants, citizen

groups, and state and local agencies. Aquatic plant diver-

sity and abundance are important indicators of lake or

wetland health, but accurate maps and data are difficult to

acquire. Because ground-based mapping requires much

time and human resources, only a small fraction of this

large resource has been mapped by natural resource

agencies.

The principal objective of this study was to evaluate the

capability of high-resolution satellite imagery to map and

classify aquatic plant groups for use by resource manage-

ment agencies. To do the evaluation using IKONOS imag-

ery, we conducted an aquatic plant survey on Swan Lake in

Nicollet County, MN. Swan Lake is a large (>3600 ha),

‘‘type-4’’ wetland meaning it is classed as a deep fresh water

marsh with standing water and abundant aquatic vegetation.

To evaluate the use of QuickBird imagery for assessment of

aquatic plants in open water lakes, we conducted aquatic

plant surveys of three lakes south of Lake Minnetonka. The

lakes include Christmas Lake (104 ha) mostly in Hennepin

County, and West Auburn (55 ha) and Shutz (38 ha) Lakes

in Scott County, MN.

5.2. Methods

We adapted methods typically used for land cover clas-

sification (Lillesand et al., 1998) and developed for water

clarity assessments (Olmanson et al., 2002) to do the

evaluation. The aquatic plant classification methods con-

sisted of two procedures: separation of image features into

discrete units and classification of the pixels in each unit.

5.2.1. Satellite image data

For this study, we used an IKONOS image acquired on

September 1, 2001 of the Swan Lake area and a QuickBird

image acquired on July 28, 2002 of the Lake Minnetonka

area. An acquisition window of July 15 through September

15 captures the presence of aquatic vegetation in Minnesota.

The images were of high quality with only minor cloud

cover over the southern portion of the IKONOS image. We

applied a resolution enhancement of the multispectral bands

using the panchromatic band. Atmospheric correction or

normalization of the imagery was not necessary for the

methods used in this study.

5.2.2. Aquatic vegetation reference data

Due to the size of Swan Lake and the abundance of

aquatic plants in the lake, the collection of reference data

would be very difficult without the aid of modern technol-

ogy. We used Global Positioning System (GPS) technology

and the advanced GPS tracking software in ERDAS Imag-

ine 8.5. We collected field reference data shortly after

acquiring the imagery using a Fujitsu pen computer. While

in the field, we identified different types of aquatic vegeta-

tion and located them directly on the IKONOS image using

the field computer. Being able to accurately identify specific

locations on the image while in the field was especially

useful on this large wetland. Having the image available

quickly after its acquisition for use in reference data

collection was also a significant advantage in field sampling

because we could identify unique areas with different

spectral-radiometric responses on the image and target them

for field identification. We targeted emergent vegetation for

the evaluation, but also noted the location of submerged

vegetation appearing at the surface.

For the three lakes south of Lake Minnetonka the

Minnesota Department of Natural Resources collected the

field data. For this study the field data specifically targeted

submerged plants using an echo sounder equipped with a

GPS unit. The hydroacoustic survey collected data pertain-

ing to lake depth and plant depth at thousands of points in

the shallower portions of the lakes where aquatic plants

were present but not at the surface. General point survey

data were collected in areas where the submerged plants

were topped at the surface or where there were floating or

emergent plants.


5.2.3. Classification procedures

The first step for the Swan Lake IKONOS image was to

separate wetland features from terrestrial features by digi-

tizing the aquatic terrestrial boundary around the entire

wetland and all islands. We identified this boundary using

spectral-radiometric differences and spatial patterns visible

on the image. We then subset the image with the wetland

polygon to mask out all terrestrial features and create a

wetland-only image.

Swan Lake has a maximum depth of 2 m, clear water

throughout, and an abundance of aquatic vegetation. Con-

sequently, we assumed that aquatic vegetation was present

throughout the wetland. An aquatic vegetation survey con-

ducted by Tyler and Madsen (personal communication), in

which the presence or absence of 27 species of aquatic

plants at 118 evenly distributed sample points on the lake

was recorded, verified this assumption. The next step was to

stratify the wetland into emergent and submergent vegeta-

tion by performing an unsupervised classification specifying

10 classes. Emergent vegetation had very different spectral

characteristics from most submerged vegetation and were

put into distinct classes easily identified from the locations

where they occurred. We masked out submerged vegetation

features to create an emergent vegetation image, and con-

ducted a second unsupervised classification on the emergent

image using 100 classes. Because of the difficulty in

separating some emergent plant types and our finding that

some areas with very thick submergent plant mats floating

on the surface were included in the image, we conducted

further ‘‘cluster busting’’ by stratifying the image further

Fig. 8. IKONOS image classification of aquatic vegetation of Swan

into two emergent images and a thick submergent image.

We performed a third level of unsupervised classifications

using 100 classes for the emergent vegetation images and 10

classes for the thick submergent vegetation image. Using the

field reference data, we identified five different emergent

classes on the emergent vegetation images and recoded the

images to create an emergent vegetation map. We repeated

this procedure for the thick submerged vegetation image and

identified two submerged classes.

Next, we created a submerged vegetation image by

masking the emergent vegetation features. We clustered

the submergent vegetation image into 10 classes to identify

different types of submergent aquatic vegetation. Using a

graph of the spectral-radiometric signatures of the different

classes and the reference data, we identified classes of

different aquatic plant densities. Assuming that water clarity

is similar throughout the wetland and that aquatic plants are

located throughout the wetland, we attributed the differences

in spectral response to differences in submerged aquatic

plant depth and densities. We identified classes with higher

radiometric response as areas where the submerged vegeta-

tion was highly dense and appeared at the surface, and areas

with lower radiometric response as areas where submerged

vegetation was deeper or thinner. We created the submerged

vegetation map by combining the two submerged plant

images and recoding the map into four different submerged

plant density classes. Finally, we created an aquatic plant

classification map by overlaying the submerged aquatic

plant map and the emergent aquatic plant map over the

panchromatic image (Fig. 8).

Lake in Nicollet County, MN (Imageryn Space Imaging L.P.).

Table 2

Accuracy of IKONOS image classification of aquatic vegetation for Swan

Lake, MN

Classified Reference data

dataCattail Sedge Sedge,

dead

Bulrush Lily Submerged Users

accuracy

(%)

Cattail 24 3 88.9

Sedge 4 2 66.7

Sedge,

dead

5 100.0

Bulrush 7 100.0

Lily 13 1 92.9

Submerged 4 3 4 13 54.2

Producers

Accuracy (%)

100.0 36.4 62.5 53.8 100.0 92.9 79.5


We followed similar procedures for the three lakes south

of Lake Minnetonka with the exception of also stratifying

the open water areas before classification of the submerged

and emergent/floating aquatic plants.

Stratification and unsupervised classification of the res-

olution-enhanced multispectral high-resolution imagery pro-

vided us with high-resolution maps that identify different

aquatic vegetation groups throughout the Swan Lake and

three lakes. The accuracy evaluation of the lake assessments

is ongoing and will be included in subsequent papers. To

evaluate the accuracy of the Swan Lake aquatic vegetation

map, we had two datasets available. The first is the field

data we collected and used to classify the imagery. Unfor-

tunately, these locations were not random and are biased by

our prior knowledge of the aquatic vegetation types.

The other dataset, collected by Tyler and Madsen (un-

published), has 118 evenly distributed data points for which

they identified the presence or absence of 27 species of

aquatic plants. In reviewing the locations of these data, it

became clear that some points were not accurately posi-

tioned on the map. For example, the GPS coordinates of

some aquatic plant points were in forested areas at least 50

m away from the nearest aquatic plants. Therefore, instead

of using this survey for an accuracy assessment, we used it

to compare the statistical distribution of plant species in

Swan Lake.

5.3. Results and discussion

The final classification map for Swan Lake with five

classes of emergent and four classes of submerged aquatic

vegetation is shown in Fig. 8. Qualitative comparison to the

field observations that were initially acquired with the

imagery indicate that the classes of vegetation have been

mapped quite accurately. There was strong agreement be-

tween the field survey of plant distribution by Tyler and

Madsen and the IKONOS classification (Table 1). We also

have quantified the accuracy in an error matrix for the

emergent classes and the submerged class as a whole (Table

2). The overall accuracy was 79.5%, with producers and

users accuracies from 36% to 100% for the individual

classes. The highest accuracies occurred with plants that

grow in homogeneous dense beds such as cattail and water

lily/floating leaf pondweed. Plants that tend to have sparse

growing characteristics and areas with more heterogeneous

Table 1

Comparison of IKONOS and field survey estimates of the distribution of

aquatic vegetation diversity in Swan Lake, MN

IKONOS (%) Field survey a (%)

Submerged plants 63.6 58.5

Cattail 28.1 23.7

Sedge and dead sedge 2.5 5.1

Bulrush 1.6 6.8

Water lily and FLPW 4.2 5.9

a Tyler and Madsen, unpublished data.

plant communities were more difficult to classify accurately.

Ideally, the image would be taken back into the field after

classification, shortly after image acquisition, to verify the

classification using the GPS tracking software. Being able to

accurately identify specific locations on the classified image

while in the field would be especially useful for accuracy

assessments and for improving the classification.

Preliminary review of the aquatic plant assessments of

the three lakes south of Lake Minnetonka indicates that

submerged plants can be separated from open water areas

and classified to a plant top depth of around 2 m. It also

appears that submerged plants with more dense growing

characteristics like Eurasian watermilfoil (an exotic to

Minnesota lakes) can be separated from other submerged

plants that tend to have less dense growing characteristics.

Ozesmi and Bauer (2002), in their review of the methods

and results of satellite remote sensing of wetlands, primarily

with Landsat and SPOT data, found that accurately mapping

wetlands is a challenging task. However, this study indicates

that the use of IKONOS and QuickBird imagery for aquatic

plant surveys is promising. Stratification and unsupervised

classification of pan-sharpened multispectral high-resolution

imagery provided a map identifying different aquatic veg-

etation groups throughout a large type-4 wetland and three

lakes. The high spatial resolution of the imagery enables the

assessment of aquatic vegetation variation within lakes and

wetlands that cannot be obtained from Landsat data and the

multispectral data enable classification beyond what is

commonly done with aerial photography. Future work will

include object-based approaches to classification.

6. Conclusions

Through our experiences with these three projects, we

have identified a number of considerations for applications

development using high resolution satellite imagery. First,

we have had the most success applying high-resolution

imagery to local or city scale analyses. When imagery is

acquired over large geographic areas, scene differences can


and do exist due to different acquisition dates, view angles,

sun angles, and atmospheric conditions. Because of this

reality, we believe that this form of satellite data is best

suited to analyses of relatively small geographic areas (i.e.,

single images), or potentially north–south oriented areas

that could fall along a single path. In addition, the cost of

high-resolution imagery may be prohibitively expensive for

large area assessments, but it is likely affordable for many

cities since the imagery cost can be shared between depart-

ments and used for several applications.

It has become clear that point data is often insufficient

field reference to calibrate high-resolution imagery. Due to

differences in GPS coordinates and rectification accuracy,

assigning a single field reference point to a pixel is difficult.

However, improved global positioning technology has been

a timely and valuable asset in the acquisition of field

reference data for use with high-resolution imagery. It is

now possible using field computers with GPS capability and

GIS and image processing software, to take high-resolution

satellite imagery into the field and capture points and

polygon reference areas directly correlated with the imagery.

High levels of precision for both the GPS and the imagery

are imperative for this work. The clarity of the imagery also

provides for more visual interpretation than has been previ-

ously possible, allowing the analyst greater certainty in

some field referencing.

While the spatial detail of high-resolution imagery is

impressive, the problem of spectral-radiometric similarity

between certain classes is, if anything, compounded. Mixed

pixels are still present and the variability within classes may

be greater. We have identified a number of incidences of

spectral confusion along the range of digital number values

as a result of these three applications. Dark feature con-

fusions include open water, asphalt and shadows, mid-range

confusions include wetlands, shadows and forest damage

and bright feature confusions include concrete, bare fields

and recreational fields.

The presence of shadows that are readily resolved has

been a problem in virtually every application. Shadowed

portions of lakes in clarity assessments can lead to unnat-

urally clear lake estimations. Shadows from tall buildings in

an impervious surface analysis can skew estimates to greater

amounts of imperviousness than actually exist. The high

radiometric resolution of the data may provide a means to

extract information from areas that would otherwise be

obscured by shadows, but we have not yet had an oppor-

tunity to investigate this possibility. In some cases it may be

possible to obtain imagery at dates close to the summer

solstice to minimize shadows.

One of the challenges of accuracy assessment of high-

resolution classification is to match the resolution of the

reference data and the classification. As suggested above,

this can be especially challenging at the pixel level and is

one of the reasons we are interested in the use of segmen-

tation and object-based classification approaches. Reference

data that might be appropriate for evaluating moderate

resolution classifications (e.g., Landsat TM, ETM+) may

be inadequate for high-resolution imagery when individual

features within a class are being resolved.

Similar spectral relationships exist between Landsat TM

and high-resolution IKONOS and QuickBird data for appli-

cations involving continuous variables such as lake water

clarity and impervious surface area. This is a great benefit to

the development of high-resolution satellite applications as

it can build on past research. This similarity also allows an

analyst to create and apply a modeled relationship from a

similar date of Landsat data and field data surrounding or

just outside of the IKONOS or QuickBird image itself. This

ability opens new possibilities for resource assessments at

different scales and for places where it is difficult to gather

field data.

In addition to the considerations and limitations previ-

ously mentioned, we believe that applications utilizing high-

resolution satellite imagery have several advantages over

other mapping methods. While traditional methods of land

cover mapping can produce high-resolution maps, their

production is expensive and time consuming. The high-

resolution imagery classification methods provide uniform

results over a local scale at high resolution with minimal

time and effort. By mapping variables, such as lake clarity

and impervious surface area, as continuous variables, the

high-resolution satellite imagery approach provides a highly

customizable set of classes. In addition, new software

options can easily make the conversion between raster and

vector formats. High-resolution satellite imagery is GIS-

ready for additional analysis. We are also encouraged by the

potential for repeat image acquisition and processing, mak-

ing change detection possible for all three of these applica-

tions. These advantages make a high-resolution satellite

imagery-based approach to mapping and monitoring natural

resources affordable, time efficient, repeatable, and reliable.

In addition, several characteristics of satellite acquired high-

resolution data, including similarity in many aspects to

aerial photography with the added advantages of digital

format, multitemporal sequences, and multispectral imagery,

should make it attractive to natural resource managers.

The initial U.S. high-resolution commercial observation

satellites are now providing a steady source of high-resolu-

tion panchromatic and multispectral imagery data for a

broad range of commercial and government customers.

Resource managers are developing interest in utilizing

high-resolution satellite imagery to aid in their management

activities. The City of Eagan, the site of our lake water

clarity and impervious surface area research, is one example

of a promising, early participant. Obviously not every local

government unit will have the capabilities to process its own

satellite imagery, but these products will no doubt provide

impetus for commercial data distributors or value-added

companies to supply the environmental information services

such as these and others for local scale mapping and

monitoring. With increasing availability of high-resolution

satellite imagery, these approaches are feasible for local land


and water resource managers who need detailed and accu-

rate monitoring information.

Acknowledgements

We gratefully acknowledge the support of the NASA

Science Data Purchase for providing IKONOS images, the

Upper Great Lakes Regional Earth Science Applications

Center (NASA grant NAG-13-99002), and the University of

Minnesota Agricultural Experiment Station. We also

appreciate the interest and cooperation of the City of Eagan,

Metropolitan Council, Minnesota Pollution Control Agency

and Minnesota Department of Natural Resources. Melanie

Tyler and John D. Madsen, Biological Sciences Department,

Minnesota State University, Mankato, provided the aquatic

vegetation survey field data. We also thank the reviewers

whose comments helped us improve this paper. Finally, we

thank Sarah Finley for her help in compiling and editing this

manuscript.

References

Barbier, E. B., Burgess, J. C., & Folke, C. (1994). Paradise lost? The

ecological economics of biodiversity. London: Earthscan Publications,

267 pp.

Barnes, K. B., Morgan, J. M., & Roberge, M. C. (2000). Impervious

surfaces and the quality of natural and built environments. Baltimore,

MD: Department of Geography and Environmental Planning, Towson

University.

Bauer, M. E., Doyle, J. K., & Heinert, N. J. (2002). Impervious surface

mapping using satellite remote sensing. Proceedings, IEEE Geoscience

and Remote Sensing Symposium, Toronto, Canada, June 24–28, Un-

paginated CD ROM, 3 pp.

Carlson, R. E. (1977). A trophic state index for lakes. Limnology and

Oceanography, 22, 361–369.

Civco, D. L., & Hurd, J. D. (1997). Impervious surface mapping for the

state of Connecticut. Proceedings, 1997 ASPRS/ACSM Annual Conven-

tion. Seattle, WA, April 7–10 ( pp. 124–135).

Daily, G. C. (Ed.). (1997). Nature’s services: Societal dependence on nat-

ural ecosystems. Washington, DC: Island Press, 392 pp.

Estes, M. G., Gorsevski, V., Russel, C., Quattrochi, D., & Luvall, J. (1999).

The urban heat island phenomenon and potential mitigation strategies.

Proceedings, 1999 National Planning Conference: Approaching the

Millennium, Seattle, WA.

Kloiber, S. M., Brezonik, P. L., & Bauer, M. E. (2002). Application of

Landsat imagery to regional-scale assessments of lake clarity. Water

Research, 36, 4330–4340.

Kloiber, S. M., Brezonik, P. L., Olmanson, L. G., & Bauer, M. E. (2002). A

procedure for regional lake water clarity assessment using Landsat

multispectral data. Remote Sensing of Environment, 82(1), 38–47.

Lillesand, T., Chipman, J., Nagel, D., Reese, H., Bobo, M., & Goldman, R.

(1998). Upper Midwest gap analysis program image processing proto-

col. Report prepared for the U.S. Geological Survey, Environmental

Management Technical Center, Onalaska, Wisconsin, EMTC 98-

G001. 25 pp.+Appendixes A–C.

Lillesand, T. M., Johnson, W. L., Deuell, R. L., Lindstrom, O. M., &

Meisner, D. E. (1983). Use of Landsat data to predict the trophic state

of Minnesota lakes. Photogrammetric Engineering and Remote Sens-

ing, 49, 219–229.

Mitsch, W. J., & Gosselink, J. G. (1993). Wetlands (2nd ed.). New York:

Van Nostrand Reinhold, 722 pp.

Olmanson, L. G. (1997). Satellite remote sensing of the trophic state con-

ditions of the lakes in the Twin Cities Metropolitan Area. M.S. Paper,

Water Resources Science, University of Minnesota, St. Paul, MN. 28 pp.

Olmanson, L. G., Bauer, M. E., & Brezonik, P. L. (2002). Water quality

monitoring of 10,000 Minnesota Lakes: statewide classification of lake

water clarity using Landsat imagery. Proceedings, 15th William T. Pe-

cora Memorial Remote Sensing Symposium. November 8–15, Denver,

Colorado, unpaginated CD ROM, 5 pp.

Ozesmi, S. L., & Bauer, M. E. (2002). Satellite remote sensing of wetlands.

Wetlands Ecology and Management, 10(5), 381–402.

Ridd, M. K. (1995). Exploring a V-I-S (vegetation-impervious surface-soil)

model for urban ecosystem analysis through remote sensing: compara-

tive anatomy for cities. International Journal of Remote Sensing,

16(12), 2165–2185.

Sleavin, W. J., Civco, D. L., Prisloe, S., & Giannattti, L. (2000). Measuring

impervious surfaces for non-point source pollution modeling. Proceed-

ings, 2000 ASPRS Annual Conference. May 22–26, Washington, DC,

11 pp.

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