Mapping Impervious Cover Within Charlestown, Rhode Island s. Salt Pond Region. Amanda Ryan

Transcription

1 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

2 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. 2

3 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. 3

4 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 4

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 5

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% 6

7 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. 7

8 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). 8

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 9

10 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 10

11 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. 11

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 12

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 and was based on the 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 13

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 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. 14

15 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. 15

16 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: 16

17 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 It provides helpful reference points during the editing process. Create a grid layer to create distinct areas (optional) 17

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. 18

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: 19

20 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. 20

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. 21

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 imagery. This difference can be attributed to new 22

23 development that occurred from , 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. 23

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 24

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 mi 2 SAMP area, or 6.3 hours/mi 2. This process was timeconsuming 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. 25

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. 26

27 Literature Cited Arnold, C.L. and C.J. Gibbons Impervious surface coverage: the emergence of a key environmental indicator. Journal of the American Planning Assoc. 62: Brabec, E., S. Schulte, and P.L. Richards Impervious surfaces and water quality: a review of current literature and its implications for watershed planning. Journal of Planning Lit. 16: Center for Watershed Protection, Is Impervious Cover Still Important? From Runoff Rundown, Center for Watershed Protection, Ellicott City, MD Connecticut Nonpoint Education for Municipal Officials (CT NEMO), Total Maximum Daily Load (TMDL) Project in Connecticut s Eagleville Brook Watershed Last accessed May 5, Environmental Protection Agency The quality of our nation s water. United States Environmental Protection Agency #EPA-841-S Washington, DC: USEPA Office of Water. Ernst, L.M., L.K. Miguel, and J. Willis Rhode Island s Salt Pond Region: A Special Area Management Plan. Prepared for the Rhode Island Coastal Resources Management Council. Hurd, J.D. and D.L. Civco Surface Water Quality and Impervious Surface Quantity: A Preliminary Study. NOAA Grant NA16OC2673. Lathrop, R.G. and T.M. Conway A build-out analysis of the Barnegat Bay watershed. CRSSA Technical Report Mallin, M.A. et al Effect of human development on bacteriological water quality in coastal watersheds. Eco App. 10:

28 Paul, M.J. and J.L. Meyer Streams in the urban landscape. Annu. Rev. Ecol. Syst. 32: Roy et al Stream macroinvertebrate response to catchment urbanization. Freshwater Biology. 48: Schiff, R. and G. Benoit Effects of impervious cover at multiple spatial scales on coastal watershed streams. Journal of Amer. Water Res. Assoc. 43: Stankowski, S.J Population Density as an indirect indicator of urban and suburban land-surface modifications. U.S. Geological Survey Professional Paper 800-B: B219-B224. Vanasse Hangen Brustlin, Inc. (VHB), Town of Charlestown, RI Comprehensive Plan. Zhou, Y. and Y.Q. Wang Extraction of impervious surface area using orthophotos in Rhode Island. ASPRS Annual Conference. 28