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An Assessment of Impervious Surface Areas in Rhode Island
Yuyu Zhou and Y.Q. Wang

Northeastern Naturalist, Volume 14, Issue 4 (2007): 643–650

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2007 NORTHEASTERN NATURALIST 14(4):643–650 An Assessment of Impervious Surface Areas in Rhode Island Yuyu Zhou1 and Y.Q. Wang1,* Abstract - Impervious surface area (ISA) has emerged as a key indicator to explain and predict ecosystem health in relationship to watershed development. In this study, we extracted the information of ISA for the state of Rhode Island using 1-m spatial resolution true-color digital orthophotography data. We employed an object-oriented algorithm of multiple-agent segmentation and classifi cation (MASC) that we developed for ISA information extraction. The result indicates that, as of 2004, 10% of the state land has been covered by ISA. The major population centers and historical cities, such as Providence, Woonsocket, and Newport, have ISA over 30%. The heavily settled suburban communities have ISA between 10 and 30%. Only 17 out of 39 towns in the state have less than 10% ISA. The average ISA for the coastal towns is 14%. Because most stream-quality indicators are predicted to decline when watershed ISA exceeds 10%, the results from this study serve as an alarming indicator for managing the state’s watershed and coastal ecosystems. The tested MASC model could be extended to coastal Massachusetts and Connecticut to provide a more comprehensive indication of the impacts of humaninduced land-cover change on southern New England’s coast. Introduction Impervious surface area (ISA) has emerged as a key indicator to explain and predict ecosystem health in relationship to watershed development. By defi nition, urban pavements, such as rooftops, roads, sidewalks, parking lots, driveways, and other manmade concrete surfaces, are among impervious surface types. ISA has been considered a key environmental indicator due to its impacts on aquatic systems and its role in transportation and concentration of pollutants (Arnold and Gibbons 1996). Urban runoff, mostly over impervious surface, is the leading source of pollution in US estuaries, lakes, and rivers (Arnold and Gibbons 1996, Booth and Jackson 1997). A recently published watershed- planning model predicts that most water-quality indicators for streams decline when the watershed ISA exceeds 10% (Schueler 2003). Bordered by Connecticut in the west and Massachusetts in the north, Rhode Island is a heavily settled coastal state. Narragansett Bay extends into the capital city of Providence and connects the developed coastal shoreline with the open ocean. The landscape of Rhode Island is typical of a developed southern New England state. As a result of urban development, Rhode Island experiences problems caused by urban runoff. For example, on August 21, 2003, over one million fi sh as well as other marine organisms in Greenwich Bay were killed due to low oxygen in the water (RIDEM 2003). ISA along the coastal zone was considered as one of the possible factors that triggered this incident. 1Department of Natural Resources Science, University of Rhode Island, 1 Greenhouse Road, Kingston, RI 02881-0804. *Corresponding author - yqwang@uri.edu. 644 Northeastern Naturalist Vol. 14, No. 4 Assessment of the quantity of ISA in landscapes has become increasingly important with growing concern of its impact on the environment (Civco et al. 2002, Dougherty et al. 2004, Wang and Zhang 2004, Weng 2001). ISA for the state of Rhode Island can be extracted from an existing dataset derived from classifi cation of Landsat images at 30-m spatial resolution (Novak and Wang 2004). However, there is lack of information at fi ner spatial resolution for more precise measurements of ISA distribution in the state. Using the most recent airborne digital orthophotos available at the Statewide Planning Program, we employed a multiple-agent segmentation and classifi cation (MASC) algorithm (Zhou and Wang, in press) to extract high spatial resolution ISA for the state. The MASC algorithm includes submodels of segmentation, shadow-effect, multivariate analysis of variance (MANOVA)-based classifi cation, and post-classifi cation. Riparian zones are important in hydrology, ecology, and environmental management because of their relationship with soil conservation, biodiversity, and water quality. With urban development, ISA along and near the riparian zones has been increasing quickly. Also ISA along transportation lines has greater environmental impacts than isolated ISA (Brabec et al. 2002). Therefore, we created fi ve buffer zones along the major rivers and roads to summarize the spatial distribution and patterns of ISA in the state. Methods Data preparation The Rhode Island Statewide Planning Program acquired a set of true-color digital orthophotographs between 2003 and 2004 through the National Agricultural Imagery Program (NAIP). This ortho-rectifi ed imagery dataset with 1-m ground sample distance has a horizontal accuracy of within ± 3 m of reference digital ortho quarter quads (DOQQS) from the National Digital Ortho Program. The ortho images are projected into Rhode Island State Plane Coordinate System with zone 3800 in US survey feet. The resulting spatial resolution of the dataset is 1 m (3.28 feet). The dataset has spectral bands of red, green, and blue light and is distributed in GeoTIFF format (Fig. 1a). As texture information can be helpful for spatial-information defi nition, we used a 3-x-3 pixel window to extract the variance as one of the features in the image-segmentation process. MASC algorithm for ISA extraction The MASC algorithm includes submodels of segmentation, shadoweffect, MANOVA-based classifi cation, and post-classifi cation (Zhou and Wang, in press). Segmentation defi nes regions in an image corresponding to objects in a ground scene. Successful image segmentation is the most important prerequisite in object-oriented classifi cation (Baatz and Schäpe 2000). In the segmentation submodel, the added shape information enhanced the performance of the multiple-pass segmentation algorithm that has been used in object-oriented classifi cations (Woodcock and Harward 1992). It is typical that tall objects such as buildings and trees cause shadowing effects in high spatial resolution remotely sensed images. Therefore, the shadoweffect submodel used a split-and-merge process to separate shadows and the 2007 Y. Zhou and Y.Q. Wang 645 objects that cause the shadows. In order to conduct an information-enhanced image classifi cation, the MASC imbedded a MANOVA-based classifi cation submodel to incorporate the relationship between spectral bands and the variability in the training objects and the objects to be classifi ed. Different types of ISAs were treated as corresponding objects. In the fi nal class map, we focused only on two categories: ISA and non-ISA. For suburban areas, dense tree canopies unavoidably cover some ISAs such as the road segments in orthophotos, which makes direct extraction of ISA impossible. With a GIS-supported post-classifi cation process, we employed the 1995 transportation data from the Rhode Island Geographic Information System (RIGIS) to recover missing segments of ISA caused by the shadows. Figure 1. (a) The true-color digital orthophoto with 1-m spatial resolution for the state of Rhode Island, (b) the spatial distributions of ISA, (c) the enlarged orthophoto data for Westerly, and (d) the spatial distribution of ISA in Westerly. 646 Northeastern Naturalist Vol. 14, No. 4 It took 113 scenes of orthophotos to cover the entire state of Rhode Island excluding Block Island (Fig. 1a). The 113-scene dataset covers 38 of the 39 towns of the state, with the exception being the township of New Shoreham on the Block Island. In order to extract the ISA for the state effi ciently, we developed a batch-process algorithm and applied it on the segmentation and classifi cation process. First, the algorithm segmented these image scenes using a set of the parameters. Second, we performed a pre-classifi cation stratifi cation according to the 1995 land-cover and land-use data from the RIGIS and selected the training objects of certain land-cover types for each subset from the pre-classifi cation stratifi cation. The training samples included 5 main categories: ISA, forest, grassland, bare soil, and water. Each category has several subcategories. For example, black asphalt cover and concrete pavement with different spectral features were treated as subcategories of ISA. We compared and checked these training data according to the covariance matrix of each object and used the samples with smaller covariance. Upon fi nishing classifi - cation, we recoded the results of categories into ISA and non-ISA. Finally, we applied the post-classifi cation submodel to integrate GIS data and the classifi - cation result to obtain the high spatial resolution ISA cover for the state. Pattern analysis In order to evaluate the spatial unevenness of ISA distribution, we calculated the ISA percentage in the coastal towns (i.e., the towns with coastal shorelines) and inland towns separately using the ratio of ISA areas and total areas of coastal towns and inland towns (Fig. 1a). To understand the spatial patterns of ISA in riparian zones and on road sides, we built fi ve buffers of Table 1. Percentage of ISA for inland and coastal towns in Rhode Island. Inland Coastal No. Name Area (ha) ISA (%) No. Name Area (ha) ISA (%) 1 Exeter 15,130 3 1 Little Compton 5854 6 2 Foster 13,466 3 2 Charlestown 9900 6 3 Glocester 14,726 4 3 South Kingstown 15,880 7 4 Scituate 14,201 4 4 Tiverton 7863 8 5 West Greenwich 13,271 5 5 Jamestown 2505 9 6 Hopkinton 11,437 5 6 East Greenwich 4226 11 7 Burrillville 14,760 5 7 Narragansett 3691 12 8 Richmond 10,556 6 8 Portsmouth 6115 13 9 Coventry 16,183 8 9 North Kingstown 11,444 14 10 North Smithfi eld 6448 9 10 Barrington 2227 14 11 Smithfi eld 7154 9 11 Westerly 7962 16 12 Cumberland 7319 13 12 Warren 1619 16 13 Johnston 6305 15 13 Middletown 3420 18 14 Lincoln 4915 16 14 Cranston 7492 19 15 West Warwick 2096 26 15 Bristol 2559 20 16 North Providence 1501 31 16 East Providence 3625 20 17 Central Falls 334 38 17 Warwick 9300 24 18 Woonsocket 2044 40 18 Pawtucket 2296 26 Average 7 19 Newport 2096 30 20 Providence 4873 37 Average 14 2007 Y. Zhou and Y.Q. Wang 647 Table 2. The error matrix of extracted ISA in Rhode Island. Reference Classifi ed Number Producer’s User’s totals totals correct accuracy (%) accuracy (%) ISA 96 85 83 86.5 97.7 Non-ISA 104 115 102 98.1 88.7 Totals 200 200 185 92.5 Kappa value = 0.85. Overall accuracy = 92.5%. 15, 60, 120, 300, and 600 m along the major rivers and roads, respectively, for the coastal towns based on RIGIS data. We compared the percentage of ISA within different buffer zones along major rivers for 17 out of the 20 coastal towns. The 3 excluded coastal towns have no major rivers. Results ISA in Rhode Island The results indicate that among the 38 townships in Rhode Island that the digital orthophoto covered, the 5 towns of Central Falls, Newport, North Providence, Providence, and Woonsocket have ISA ≥30%. The 5 towns of Bristol, East Providence, Pawtucket, Warwick, and West Warwick have ISA between 20% and 29%. Twelve towns have ISA between 10% and 19%. Even without orthophoto data, fi eld knowledge suggests that New Shoreham on Block Island has less than 10% ISA cover. Therefore, only 17 towns in the state have ISA less than 10% (Table 1). Overall, 10% of the land in Rhode Island is covered by ISA (Fig. 1b). Enlarged examples of the orthophoto and extracted ISA for the town of Westerly illustrate in detail the spatial distributions of ISA (Figs. 1c and 1d). We used a random-point sampling method to evaluate the classifi cation accuracies. We selected 200 samples in Rhode Island and examined the classifi cation accuracies for the ISA and non-ISA only. The overall accuracy for the correctness of extracted ISA is 92.5%, and the Kappa coeffi cient is 0.85 (Table 2). We also examined the producer’s and user’s accuracy, which are defi ned as the ratio of numbers of pixels correctly classifi ed for a certain category and total numbers of ground-reference pixels for that category, and the ratio of numbers of pixels correctly classifi ed for a certain category and total numbers of pixels classifi ed at that category, respectively. The results show that producer’s and user’s accuracies are, respectively, 86.5% and 97.7% for the ISA, and 98.1% and 88.7% for the non-ISA categories. Pattern of ISA distribution The results show that ISA distributions are spatially uneven in the state. ISA is more extensive along the coastal areas than that in the interior areas (Fig. 1b). The areas of major population centers and historical cities, such as Providence, Woonsocket, and Newport, have the highest percentage of ISA. The heavily settled suburban communities have ISA between 10 to 30%. Comparisons of the percentage of ISA in inland areas and coastal areas along the eastern and southern coast zones (Fig. 1a) reveal the spatial 648 Northeastern Naturalist Vol. 14, No. 4 patterns of ISA cover in these areas. The average ISA for the coastal and inland towns is 14% and 7%, respectively (Table 1). Calculated ISA percentage increases with buffer areas from 15 m to 600 m along the major rivers (Fig. 2). The exception is the town of Westerly (No. 11 in Fig. 2), where the ISA shows a decreasing trend with increased buffer sizes. This is because Westerly has only a very limited area covered by buffers along rivers. The decreasing trend should not refl ect the real pattern of ISA cover within the entire township. The result illustrates that, in general, areas closer to rivers are less covered by ISA and may indicate that Figure 2. The variation of ISA percentage within 5 buffer zones in the coastal towns along major rivers. Figure 3. The variation of ISA percentage within 5 buffer zones in the coastal towns along major roads. 2007 Y. Zhou and Y.Q. Wang 649 fl oodplains and other riparian environments have received some protection from zoning and wetland regulations. Unlike the patterns observed for the riparian zones, the percentage of ISA along the major roads shows a decreasing trend with the increasing buffered areas from 15 to 600 m (Fig. 3). This result indicates that most of the ISA is distributed along the road networks. Furthermore, ISA percentage decreased to the average level within the entire township as the buffer size increased to 600 m. Discussion Impervious surface area has been used as an indicator for environmental impacts of urbanization by a variety of research and education programs in the southern New England region. ISA impacts in watersheds can become very costly in terms of water quality and quantity. The National Nonpoint Education for Municipal Offi cials (NEMO) project and the Watershed Watching programs in Rhode Island, among others, all rely on information of ISA and its relationship to water systems to inform better community planning. Previous studies in Rhode Island and Connecticut employed Landsat TM and ETM satellite remote-sensing data at a 30-m spatial resolution to quantify ISA. The results indicated an increasing trend in ISA cover in southern New England coastal areas (Center for Land-use Education and Research 2003, Novak and Wang 2004). In Rhode Island, ISA increased 43% between 1972 and 1999, six times faster than population growth (Rhode Island Economic Policy Council 2006). The increase of ISA consumed large areas of forests, which are critical to the health of watersheds. The forests curb the effects of soil erosion and fl oods, purify the air, and help moderate changes in climate. The increase of ISA caused forest fragmentation as well. It is a concern because larger forests often provide the greatest environmental benefi t. While Rhode Island lost 18,000 acres of forest land from 1972 to 1999, the remaining forests are being increasingly fragmented due to increase of ISA (Rhode Island Economic Policy Council 2006). Riparian zones are important in hydrology, ecology, and environmental management. The results from buffer analysis indicate that ISA percentage increases with increasing buffer sizes along the major rivers. Road networks are major factors in contributions of urban runoff and associated pollutants. As further evidence for the impacts of roads on adjacent ecosystems, the results indicate that the percentages of ISA are the highest within the nearest buffer along road networks. Although middle spatial resolution ISA information in New England states are available, ecosystem modeling and management decision making require more precise information on ISA distribution. The research described in this paper provided the precise spatial distribution and quantifi - cation. This study developed a high spatial resolution ISA dataset for Rhode Island, which is valuable for land management, planning, and for ecological and hydrological modeling to determine the effects of urban development on coastal environments and watersheds. Because true-color orthophoto data are becoming more common among state agencies and are being widely used 650 Northeastern Naturalist Vol. 14, No. 4 by the general public, the modeling approach used in this study can be extended to the coastal areas of Massachusetts and Connecticut and provide a more comprehensive indication of the impacts of human-induced land-cover change on the southern New England coast. Acknowledgments We thank guest editor, Stephen Trombulak and anonymous reviewers for their suggestions and comments, which improved this manuscript. This research was funded by the Rhode Island Agricultural Experimental Station (Project No. RI00H330). Literature Cited Arnold, C.A., Jr., and C.J. Gibbons. 1996. Impervious surface coverage: The emergence of a key urban environmental indicator. Journal of the American Planning Association 62:243–258. Baatz, M., and A. Schäpe. 2000. Multiresolution segmentation: An optimization approach for high-quality multi-scale image segmentation. Pp. 12–23, In J. Strobl and T. Blaschke (Eds.). Angewandte Geographische Informationsverarbeitung XII. Wichmann-Verlag, Heidelberg, Germany. 542 pp. 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. Journal of Planning Literature 16:499–514. Booth, D.B., and C.R. Jackson. 1997. Urbanization of aquatic systems: Degradation thresholds, stormwater detection, and the limits of mitigation. 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Rhode Island Economic Policy Council. 2006. Community development scorecard. Available online at http://www.ripolicy.org/2010. Accessed April 2007. Schueler, T. 2003. Impacts of impervious cover on aquatic systems. Center for Watershed Protection (CWP), Ellicott City, MD. 142 pp. Wang, Y., and X. Zhang. 2004. A SPLIT model for extraction of subpixel impervious surface information. Photogrammetric Engineering and Remote Sensing 70:821–828. Weng, Q.H. 2001. Modeling urban-growth effects on surface runoff with the integration of remote sensing and GIS. Environmental Management 28:737–748. Woodcock, C.E., and V.J. Harward. 1992. Nested-hierarchical scene models and image segmentation. International Journal of Remote Sensing 13:3167–3187. Zhou, Y., and Y. Wang. In press. Extraction of impervious surface areas from high spatial resolution imageries by multiple-agent segmentation and classifi cation. Photogrammetric Engineering and Remote Sensing.