Mapping Impervious Cover Within Charlestown, Rhode Island’s

Salt Pond Region

By

Amanda Ryan

A MAJOR PAPER SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENTS FOR THE DEGREE OF MASTER OF ENVIRONMENTAL

SCIENCE AND MANAGEMENT

UNIVERSITY OF RHODE ISLAND

MAY 10, 2012

MAJOR PAPER ADVISORS: Dr. Arthur Gold & Dr. Peter August

MESM TRACK: Earth and Hydrologic Science

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Abstract

Impervious cover (IC) has become widely recognized as a reliable indicator of

urban environmental stress, particularly decreased water quality. It is key to

transporting nonpoint source pollution into rivers, streams, lakes and ponds.

Southern Rhode Island has experienced a significant increase in human

population and impervious cover associated with land development. The

consequences of the changing watershed landscape has become manifest in the

deterioration of the health of the salt ponds. Specifically, the ponds have

undergone eutrophication, fish kills, eelgrass reductions, increased levels of toxic

contaminants, and permanently closed shellfish harvest areas among other

degradations. The aim of this project is to provide the town of Charlestown, RI

with a highly accurate impervious cover Geographic Information System (GIS)

dataset within the state-designated Salt Pond region and also describe the

methods used to create this dataset so that other towns may create the same

tool. This information can be used to help enforce the Rhode Island municipal

separate storm sewer system mandate, regulate the stormwater Best

Management Practice requirement mandated by RI’s Stormwater Design and

Installation Standards Manual which is based on land parcel percentage IC, and

provide town planners with a baseline IC for future zoning regulation

amendments. In general, this dataset offers the town of Charlestown a unique

GIS dataset to assist their efforts to improve the health of their salt ponds.

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I. Introduction

The salt pond watersheds of southern Rhode Island have experienced significant

suburbanization beginning in the 1950’s. The growth rate has been high, with a

69% increase in population from 1981 to 1992 and the region continues to attract

new residents (Ernst et al., 1999). As a result, Rhode Island’s ecologically

important salt pond environments have become degraded. The increase in

human population and land development has ultimately resulted in the

deterioration of salt pond water quality.

A. Impervious Cover

Impervious cover, also referred to as impervious surface, is one of the most

consistent and pervasive aspects of a developed landscape. It is defined as any

material that prevents the infiltration of water into the soil and includes rooftops,

roads, sidewalks, parking lots, compacted soil and any other impenetrable

surface (Arnold & Gibbons, 1996). As development increases, so does the area

of impervious cover; it has been shown that an area’s population density is

closely correlated to the amount of impervious cover (Stankowski, 1972). The

figure below highlights the basic relationship between urbanization, impervious

cover, and the resulting environmental impacts.

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Figure  1.  Hydrologic  impact  of  urbanization.  Gray  boxes  identify  impacts  directly  related  to  impervious  surfaces  (Hurd  &  Civco,  2004)  

The environmental impacts of impervious cover can be divided into four

categories: hydrological, physical, biological, and water quality.

Hydrological Impacts

The alteration of the hydrologic cycle, the way which water is transported and

stored, begins when runoff reaches an impervious surface. By causing the

volume and velocity of surface runoff to increase, both shallow and deep

infiltration into the ground are reduced which may cause the water table to

subside, leaving less groundwater available to streams, vegetation, and for

  5  

human use (Paul & Meyer, 2001). The figure below illustrates the impacts of

increasing percentages of impervious cover within a landscape. In a natural

environment, approximately 10% of rainwater will runoff the land surface, 50%

with infiltrate the ground, and 40% is returned to the atmosphere via

evapotranspiration. Even a small increase in impervious surface, 10-20%, will

cause the surface runoff volume to double. A 1 acre paved parking lot produces

16 times the amount of runoff as a similarly sized undeveloped meadow (Center

for Watershed Protection, 2002).

 

Figure  2.  Changes  in  site  hydrology  with  increasing  impervious  cover  (EPA,  1994).

Impervious cover reduces baseflow, or the groundwater seepage into stream

channels, which sustains streams during dry periods. Increasing surface runoff

  6  

also exacerbates flooding severity by decreasing the time it takes to reach peak

flow and increasing the volume of peak flow, because less water is infiltrating the

ground (Paul & Meyer, 2001).

Physical Impacts

Increased runoff also leads to the physical alteration of the environment. Land

development leaves less tree and vegetative cover, so there is a decreased

capacity for soils to be held in place. A greater amount of sediment and debris

from construction sites, stream banks, and non-vegetated soils is transported

downstream due to the greater erosional forces of larger volumes of faster

flowing stormwater. The resulting sediment loads will carve out wider and

straighter stream channels, only to further increase the velocity of water flow

during the next storm. These conditions also damage the riffle and pool

structures, which are ecologically important stream habitats. Paul & Meyer (2001)

showed that stream channels begin to show observable widening in a landscape

with as little as 2% impervious cover.

In addition, the reduced tree and vegetative cover can reduce regulation of

stream temperatures throughout the year, which may result in a greater range

that is unsuitable for certain inhabitants (Arnold & Gibbons, 1996).

Biological Impacts

There have been many studies investigating the impact to biological indicators

within streams flowing through watersheds having various levels of impervious

cover (Schiff & Benoit, 2007). Generally, results show that a watershed with 10%

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or greater impervious cover will have definitively degraded water bodies. The

relationship between stream health and percent impervious cover within a

watershed is shown in Figure 3.

 

Figure  3.  General  relationship  of  imperviousness  to  stream  health.  (Arnold  &  Gibbons,  1996).

Schiff and Benoit (2007) found that at multiple spatial scales (watershed, local

contributing area, and the 100-m riparian buffer for each) a total impervious area

(TIA) as low as 5% had a negative impact on stream water quality,

macroinvertebrate community assemblage, and in-stream habitat. Conditions

worsened with a TIA of up to 10% and remained constant with increasing

percentages of TIA. Spatially, both the water and habitat quality had a strong

correlation to TIA at all scales, while the macroinvertebrate showed a relatively

weaker relationship to TIA at larger spatial scales.

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A literature review of impervious surface and water quality by Brabec et al.

(2002) reports a wide variation amongst the dozens of studies reviewed

regarding the percentage IC when certain aspects of stream health will begin to

degrade, from 4% - 50%. However, the studies specifically investigating a

stream’s biotic integrity, or aquatic species richness and composition, had ranges

much lower, from 4% -15% IC, suggesting this an extremely sensitive indicator.

Water quality tended to be less sensitive and its degradation threshold ranged

from 7.5% - 50% impervious cover.

Water Quality Impacts

The alteration of the hydrologic cycle also impacts the ecology of an area in

several ways. Nonpoint source pollution has been identified as one of the

greatest contributors to water quality degradation to U.S. rivers, lakes, and

estuaries. (EPA, 1994). Stormwater is known to be a major transporter of

nonpoint source (NPS) pollution, carrying pollutants such as pathogens and

excess nutrients from lawns and toxic contaminants and debris from roads and

parking lots into coastal waters (Mallin et al., 2000). When runoff infiltration is

reduced, these pollutants bypass natural degradation processes that occur as

water percolates through the soil. Surface waters receiving high levels of NPS

pollution often undergo eutrophication, a response by ecosystems to an

excessive concentration of nutrients, which can reduce biodiversity and cause

phytoplankton blooms and fish kills (Ernst et al., 1999).

  9  

Total maximum daily load (TMDL) is a tool that States are required to use under

the Clean Water Act to limit the quantity of pollutants entering an impaired water

body from both point sources and nonpoint sources of pollution. The TMDL

represents the amount of pollution a water body can accept without adversely

impacting wildlife, recreation, or other public uses. States typically calculate

TMDLs by reviewing water quality monitoring data and watershed modeling.

A pilot project conducted by the Connecticut Department of Energy and the

Environment (DEEP), the University of Connecticut, and the town of Mansfield,

CT studied the use of IC to determine a watershed’s TMDL. The project was

based on research done within the state, collecting samples from 125 stream

segments to determine the composition of the benthic macroinvertebrate

populations, which was used as an indicator of stream health. These data were

compared with the watershed impervious cover estimates. None of the stream

segments having greater than 12% impervious cover within their watersheds met

Connecticut DEEP’s aquatic life standards for a healthy stream. (CT NEMO,

2012) Other studies have found similar results, showing a negative relationship

between catchment urbanization and various biotic indices such as in-stream

taxon richness, EPT richness (an index based on the total number of taxa in

three distinct insect orders) and the Invertebrate Community Index (Roy et al.,

2003). The results of the CT DEEP study provided the basis for a new IC-based

TMDL for impaired waterways in Connecticut.

An IC-based TMDL encourages the use of Low Impact Development (LID)

strategies to reduce the impact of runoff carrying NPS pollution to water bodies

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and degrading their water quality. The primary strategies to mitigate excess

runoff and its associated pollution to receiving waters are to disconnect IC from

the drainage system, reduce or remove IC where possible, and treat runoff (CT

NEMO, 2012).

B. Using Impervious Cover as an Environmental Indicator

Knowing the percentage of a watershed that is developed or impervious can help

planners make informed land use decisions. Local planners require a simple tool

to determine the impacts of development on the environment and water

resources. Impervious cover can be used as a quantifiable environmental

indicator because it is a major contributor to the adverse environmental impacts

of urbanization. Many studies have identified a strong correlation between

percent IC in a landscape and negative impacts to the quality of receiving waters

(Schiff & Benoit, 2007; Arnold & Gibbons,1996; Wang et al., 2007).

An advantage of using IC as an environmental indicator is it provides an estimate

of the effective impact to water resources by humans without requiring extensive

data collection or analysis. Another benefit is that it is measureable, which makes

it a useful option for planning and regulation (Arnold & Gibbons, 1996).

Lathrop and Conway (2001) used impervious cover as an indicator of nonpoint

pollution when developing a build-out analysis for a Barnegat Bay watershed in

New Jersey. A build-out analysis maps the expected extent of maximum

development given existing zoning regulations. Using IC as a surrogate indicator

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for NPS pollution, these researchers were able to make future estimates of NPS

pollution impacts that otherwise would have been difficult to predict.

C. Salt Pond Region Special Area Management Plan (SAMP)

Salt ponds are shallow, productive lagoons that are separated from the ocean by

barrier spits (Ernst et al., 1999). They provide many valuable ecosystem services

such as habitat for recreational and commercial fin and shellfish, migratory

waterfowl habitat, and productive eelgrass beds (Ernst et al., 1999). In southern

Rhode Island, efforts to improve the degraded quality of the surface and

groundwater entering the salt ponds have been ongoing since the early 1980’s.

The Rhode Island Coastal Resources Management Council is responsible for

developing management plans for the protection and enhancement of the state’s

coastal resources. A growing population and continued land development within

these areas began noticeably stressing the pond’s ecosystem functions in the

1970’s, which was the impetus for the initial 1984 Salt Pond SAMP. (Ernst et al.,

1999)

The Salt Pond SAMP was developed for the region extending from the barrier

spits separating the ponds from the ocean to the inland boundary of the

individual pond’s watershed. The figure below shows the extent of the Salt Pond

SAMP within the town of Charlestown, RI.

  12  

 

Figure  4.  A  map  of  the  Rhode  Island  Salt  Pond  Region  SAMP  within  Charlestown,  RI,  classified  by  intensity  of  development.  (Ernst  et  al.,  1999)

Charlestown included the 1984 Salt Pond SAMP recommendation of increasing

the minimum residential lot size to 2 acres in their 1991 Town Comprehensive

Plan (VHB, 1991). The initial SAMP was effective at limiting the potential extent

of development and pollution sources, however, the cumulative impacts of

nonpoint source pollution, particularly bacteria and Nitrogen, resulted in salt pond

eutrophication and permanent shellfish closures (Ernst et al. 1999). In the 1999

Salt Pond SAMP revision, stormwater runoff mitigation has been listed among

  13  

the top priorities for salt pond restoration, because it is not only a major

transporter of both excess nutrients and bacteria, but also sediment, road salt,

heavy metals and petroleum hydrocarbons into the salt ponds and their

tributaries.

II. Purpose

The purpose of this project is to provide the town of Charlestown, RI with a more

accurate impervious cover GIS dataset within the state-designated Salt Pond

region. This dataset can be used to help enforce the Rhode Island municipal

separate storm sewer system mandate, regulate the stormwater Best

Management Practice requirement of RI’s Stormwater Design and Installation

Standards Manual which is based on land parcel percentage IC, and provide

town planners with a baseline IC for future zoning regulation amendments.

III. Methods

The dataset created for this project was completed using ArcGIS 10

(Environmental Systems Research Institute, Redlands CA). ArcGIS 10 is a

geographic information system that allows users to map and analyze geospatial

data. GIS is a commonly used tool in the field of environmental science. The

Rhode Island Geographic Information System (RIGIS) Impervious Surfaces

dataset was developed in 2006-2007 and was based on the 2003-2004

orthorectified aerial photography for Rhode Island. It is a raster dataset made up

of two classes, pervious and impervious land cover. This dataset was derived

using semi-automated methods and is available to be downloaded for free on the

  14  

RIGIS website. This is an excellent resource for documenting the extent of

Rhode Island’s impervious cover and is best suited for state or town-level

analysis. On a larger scale, such as at the neighborhood level, the inaccuracies

of the dataset become apparent.

The following procedure was used to manually update the RIGIS Impervious

Surface dataset for the Charlestown, Rhode Island Salt Pond region:

Create a geodatabase

• In ArcCatalog, right click the folder where you want the new geodatabase to

be stored.

• Select New > File Geodatabase

o Choose a meaningful name without any blank spaces or non-alpha

numeric characters other than dash or underscore.

Prepare your area of analysis

• Download the RIGIS Impervious Surfaces raster dataset from

http://www.edc.uri.edu/rigis/data/ under the Environment and Conservation

section.

• Next, right click the new geodatabase in ArcCatalog and select Import >

Raster dataset and browse to the location where the Impervious Surfaces

dataset is saved.

• Open a new map document in ArcMap and add the Impervious Surfaces

raster dataset to the map.

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• Also add the feature class or shapefile of the extent of the area to be

analyzed to your map.

Clip the Rhode Island Impervious Surface raster dataset

It is a good idea to clip the Impervious Surface dataset to your study region early

in the process because it is a large dataset.

• In ArcMap, open the ArcToolbox window > Data Management Tools >

Raster > Raster Processing > Clip.

o In the Clip wizard, for Input Raster, select the Impervious Surface

dataset.

o Select the area of analysis file for Output Extent.

o Check the Use Input Features for Clipping Geometry box.

o Save the new dataset into the geodatabase.

Reclassify raster dataset

• Open the Catalog Window and find the geodatabase containing the

clipped IC raster dataset. Drag this file into the empty ArcMap screen.

• Next, open the ArcToolbox window and expand the Spatial Analyst Tools

Toolbox > Reclass > Reclassify.

o In the Reclassify wizard, select the clipped impervious cover

dataset for the Input Raster line.

o Select Value from the Reclass Field dropdown menu.

o Under the New Value column, assign NoData for the top value and

1 for the bottom value.

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o Choose the geodatabase as the location for the file, select a new

name for this file and select OK.

Convert raster dataset into a vector dataset

• In ArcMap 10, open the ArcToolbox window and expand the Conversion

Tools Toolbox > From Raster > Raster to Polygon.

o In the Raster to Polygon wizard, select the final reclassified

impervious surface raster dataset for the Raster input line.

o Name the output vector file in the second line.

o Check the Simplify Polygons box to get smooth polygons rather

than shapes that follow the raster pixel borders and select OK.

o The resulting data layer will be used for editing the impervious

surfaces.

o Add this data layer to the map. Right click the layer in the Table of

Contents window and select Properties. Under the Symbols tab,

you can change the appearance of the polygons. For this project, I

choose to outline the impervious polygons with a thin red line for

maximum visibility of the underlying aerial photograph.

Other data layers

In addition to the impervious surface vector dataset created in the previous step,

there are a few other data layers used to do this analysis.

• Base Maps:

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Base maps are used as background images and can be directed

streamed within ArcMap from online sources rather than downloading

these large images onto your computer. The base maps used for this

project were the Rhode Island 2008 digital aerial photography and the

Rhode Island 2011 RIDEM digital true color orthophotography.

o To use online base maps in ArcMap, find the Add Data icon at the

top of the screen and select ‘Add Data from ArcGIS Online…’

o Type ‘Rhode Island’ in the search window to view available online

base maps for the state.

• Charlestown SAMP boundary

o The SAMP boundary data layer was obtained from the town of

Charlestown GIS specialist. It was used to clip the RIGIS

impervious surface dataset and serves as the boundary for this

project.

• E-911 Sites

o This dataset is available online from the RIGIS website. It is a point

dataset and provides basic information for each significant structure

in Rhode Island, such as address and owner and is up-to-date as of

March 2012. It provides helpful reference points during the editing

process.

Create a grid layer to create distinct areas (optional)

  18  

This technique may be useful as a guide while examining each impervious

surface polygon.

• Right click in an empty area within the upper portion of the screen near the

ArcMap toolbars. A list of optional toolbars will appear, select both Draw

and Editor.

• There is an icon that looks like the outline of a square, select this and

select an appropriate shape, for this project the square was used.

• Draw a large square covering the entire region to be edited and ensure it

is selected.

• Opening the Drawing dropdown menu, select Convert Graphics to

Features. Identify the coordinate system and use the geodatabase as the

location for this new feature class and click OK. Select Yes in the following

window to add this shape as a new layer on your map.

• Using the Editor toolbar, select Start Editing. Select the feature, in this

case, the rectangle and click on the Cut Polygons icon in the Editor toolbar.

This allows you to divide the polygon into various sections. These sections

can be symbolized in unique ways so that they stand out and it makes it

easy to see when a section border has been reached. For this project, the

rectangle was divided into smaller squares, each made 90% transparent

with a unique color. Alternatively, each square could be outlined with a

uniquely colored border to avoid tinting the aerial photo base maps. Within

the attribute table for this layer, a new field was created and each square

was labeled with a unique number.

  19  

Editing polygons

Once all the data layers are saved in the geodatabase and available within the

ArcMap document, the process of analyzing the impervious surfaces of the study

area can begin.

• Create a new field in the attribute table and name it ‘Altered’. Assign 0 to

the entire column before editing. Every time a specific polygon is edited

change the 0 value to a 1. This will help keep track of how many edits

have been made.

• Prior to editing, determine the general path you will follow to review each

impervious polygon and how you will use the grid layer as a reference.

• The scales used for this project ranged from 1:700 and 1:1,000. Choosing

the scale is a balance between efficiency and ability to clearly distinguish

the boundaries between impervious and pervious surfaces.

• It was necessary to use both the 2011 aerial photos and 2008 aerial

photos. The 2011 photos offered the most recent images and the 2008

photos offered the highest resolution. Each polygon was examined over

each set of photography.

• Select Editor on the Editing toolbar and select Start Editing. Using the

feature selection arrow, click on the impervious surface layer. Now you

can use any of the editing tools to reshape the data layer to more closely

match the aerial photograph underneath.

• In order to edit polygons as consistently as possible, I developed a list of

rules for determining how to categorize surfaces. My guidelines were:

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o Any driveway is impervious, even if not paved.

o Boats are not impervious surfaces.

o Decks and patios are impervious surfaces.

o If the impervious surface layer of a road is shifted but still

represented an accurate impervious area, it was left unedited.

Comparing completed dataset with the original RIGIS Impervious Surface

dataset

One way to analyze the new dataset is to compare it to the original dataset in

ArcMap by combining both maps. The result provides one dataset with four

distinct categories: areas where both maps were pervious, areas where only the

RIGIS dataset was impervious, areas where only the manually-edited dataset

was impervious, and areas where both datasets were impervious.

Convert vector dataset into a raster dataset

• Open ArcToolbox and expand the Conversion Tools toolbox > To Raster >

Feature to Raster.

• Enter the edited impervious surface dataset into the Input Features field.

• Select Value for the Field line. This will be the only value transferred into

the Attribute Table of the new raster dataset.

• Select your File Geodatabase for the destination and a unique name in the

Output field.

• Enter the same cell size used in the original dataset, in this case it was 2,

and select OK.

  21  

Reclassify the edited impervious surface dataset

• In ArcToolbox, expand Spatial Analyst Tools > Reclass > Reclassify.

• Use the raster created in the step above as the Input Raster.

• Select Value in the Reclass Field.

• Change the Old Value of 1 to a New Value of 10 and the Old Value of

NoData to a New Value of 0.

• Select your File Geodatabase for the destination and a unique name in the

Output field and select OK.

Clip the dataset

• In ArcToolbox, expand Data Management Tools > Raster > Raster

Processing > Clip.

• Enter the reclassified raster from the step above into the Input Raster field.

• Select the feature class representing the extent of your study area in the

Output Extent field, in this case it was the Charlestown SAMP region.

• Leave the default values in the Rectangle fields.

• Select your File Geodatabase for the destination and a unique name in the

Output field.

• Check the ‘Use Input Features to Clip Geometry’ Box and select OK.

Reclassify the original dataset

• In ArcToolbox, expand Spatial Analyst Tools > Reclass > Reclassify.

• Use the original impervious surface raster dataset for the Input Raster.

  22  

• Select Value in the Reclass Field.

• Leave the Old Value of 1 as the New Value and change the Old Value of

NoData to a New Value of 0.

• Select your File Geodatabase for the destination and a unique name in the

Output field and select OK.

Clip the dataset

• Use the same procedure as outlined above for the edited-dataset.

Combine the datasets

• Go to ArcToolbox > Spatial Analyst Tools > Map Algebra > Raster

Calculator.

• Double click on the reclassified and clipped original dataset.

• Click on the ‘ + ’.

• Double click on the reclassified and clipped edited dataset.

• Select your File Geodatabase for the destination and a unique name in

the Output field and select OK.

IV. Results and Discussion

By comparing the edited version of the impervious cover dataset to the original

RIGIS impervious dataset, I calculated the area change in IC and noticed trends

that emerged between the two datasets. First, I found that my impervious dataset

had 35 more acres of impervious cover than the original dataset, which was

based on 2003-2004 imagery. This difference can be attributed to new

  23  

development that occurred from 2004 - 2011, omissions of impervious surface by

the original dataset, and overestimates of impervious cover in my dataset.

Common omissions of the original dataset were small structures like sheds and

patios, although this is not likely a great contributor to the difference in

impervious area between the datasets. By looking at the summation dataset, it

appears that new development contributes most to the increase in area of

impervious cover.

On the other hand, the original dataset often mapped foot trails, small paths, and

sometimes stonewalls as impervious surfaces while my dataset did not. It was

less likely that an isolated structure in a less developed area would be missed in

the original dataset than the manually edited version.

  24  

 Figure  5.  Example  snapshot  of  the  summation  dataset  created  by  using  the  Raster  Calculator  

tool  to  combine  both  datasets.  

Figure 5 above illustrates many of the common patterns between the two

datasets. The red areas indicate impervious cover only delineated in the

manually edited dataset. This area includes many small sheds, small additions to

  25  

established structures, and larger areas, which were typically found along Route

1. The larger red and green area near the center of the figure is an example of

the original dataset’s underestimation of impervious areas where tree cover

overhangs impervious roads, parking lots and homes. The series of short purple

diagonal lines are dirt rows between strips of vegetation. For the edited dataset,

I chose to map these as pervious along with similar features like small trails.

It took an estimated 80 hours to complete all the impervious cover polygons

within the 12.66 mi2 SAMP area, or 6.3 hours/mi2. This process was time-

consuming and may not be a reasonable project for town GIS specialists.

Factors like computer-processing speed, size of the area being mapped, and

complexity of the impervious surfaces would likely have a significant impact on

the time needed to complete the project. It may be a good task to set aside for

interns or divided into small areas to be worked on as time permits.

Considering the large time commitment to manually develop this dataset, I

calculated the percentage of IC from each dataset: 9.3% for the original dataset

and 9.7% for the manually edited dataset. With a percent difference of 0.4%, this

method is definitely not recommended for large or even moderately sized areas.

However, this still may be worth the effort on the parcel level. This analysis

resulted in an impervious surface data layer of higher accuracy for the

Charlestown Salt Pond region than provided by the original layer for the State of

Rhode Island. While not a quick process, this data layer provides valuable

impervious surface data for the town to use as a baseline inventory and for

regulating development based on changes of impervious cover per parcel.

  26  

Acknowledgments

I would like to thank Lorraine Joubert for informing me of this project and

connecting me with the right people to get started. I would also like to

acknowledge Steve McCandless from the Town of Charlestown, RI for providing

direction, being available for assistance, and for the initial shapefiles to get

started. Dr. Art Gold took the time to discuss many possible project options

throughout my MESM career and provided guidance on the structure for this

paper. Finally, Dr. Pete August helped me plan the project methods and

reviewed and provided guidance for this paper. I’m also very thankful for his

understanding and the extension offered when the first version of the impervious

dataset was lost. Thank you for all the support.

  27  

Literature Cited

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emergence of a key environmental indicator. Journal of the American Planning

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Brabec, E., S. Schulte, and P.L. Richards. 2002. Impervious surfaces and water

quality: a review of current literature and its implications for watershed planning.

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Center for Watershed Protection, 2002. Is Impervious Cover Still Important?

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