Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography
Abstract
:1. Introduction
2. Methods
2.1. Study Site and Data
2.2. Delineating Impervious Surfaces from Aerial Photos
2.3. Landsat Image Processing
2.4. Validation Points and Accuracy Assessment
2.5. Assessment of Accuracy at the Census Tract Scale
3. Results
3.1. Aerial Image Delineation of Impervious Surfaces
3.2. Accuracy Assessments of the NLCD IS Data Set
3.3. Accuracy Assessments of the Supervised Classifications
3.4. Comparison of Aerial Photos to Supervised Classifications
3.5. Supervised Classification Results—Finer Scale
4. Discussion
Parcels
- ▪ Parcel sizes, range of parcel sizes within a specific tract, the percent impervious, and the spatial pattern of land uses all affect accuracy;
- ▪ Units with very high and very low percent impervious show high accuracies for all methods (Census Tracts 11 and 28); and
- ▪ Units with the highest percentages of impervious surfaces (Census Tract 11), show low kappa values; the more variability in range of parcel sizes within a census tract and the higher percent impervious, the greater variability in accuracy of classifications between the methods.
Classification Strategy
- ▪ At least one classification method achieved an accuracy of over 70% in nine of the eleven census tracts;
- ▪ Parallelepiped classification—bands 2–6 proved the most effective method in tracts with moderate percentages of impervious surfaces and larger average parcel sizes;
- ▪ In some census tracts, multiple methods achieved accuracies higher results greater than the city-wide NLCD IS standard; and
- ▪ For all census tracts, the most effective method included either the thermal channel (band 6) or the blue visible channel (band 1).
5. Conclusions and Future Outlook
Method as Identified in Paper | Location (Image Date) [Reference] |
---|---|
Artificial neural networks | State of Connecticut, USA (1995) [10]; Central New York State, USA (2001) [24] |
Linear segmentation | Bangkok, Thailand (1987) [25] |
Subpixel analysis | Charleston, SC, USA (1990) [26]; DuPage and Cook Counties, IL, USA (1997) & East Greenwich, RI, USA (1997) [27]; State of Missouri, USA (1980, 1990 & 2000) [28] |
Linear mixture analysis | Columbus, OH, USA (1999) [29] |
NDVI | Atlanta, GA, USA (1979, 1987, &1997) [30]; Fairfax County, VA, USA (1990, 1995, & 2000) [31]; Montgomery County, MD, USA (1990–2001) [32] Tampa Bay, FL, USA (1991–2002) [33] Seattle-Tacoma, WA, USA (1986 & 2002) and Las Vegas, NV, USA (1984 & 2002) [34]; Cixi County, China (1987, 2000, 2002 & 2009) [35] |
Regression tree | Sioux Falls, SD, USA (2000), Richmond, VA, USA (1999), and the Chesapeake Bay, USA (1999–2001) [23]; Western Georgia, USA (1993 & 2001) [36] Washington DC-Baltimore, MD, USA (1984–2010) [37] |
Spectral mixture analysis | Columbus, OH, USA (1999) [38]; Marion County, IN, USA (2000) [39]; Lake Kasumigaura, Japan (1987, 2000, & 2007) [40] Franklin County, OH, USA (1999) [41] |
Regression analysis with tassel cap | Twin Cities Metropolitan Area, MN, USA (1986–2000) [19]; State of Minnesota, USA (1990 & 2000) [42]; Franklin County, OH, USA (1999, 2000, & 2003) [43] |
Support vector regression using sub-pixel classification | Germany (1999–2001) [44] |
Object oriented classification and Normalized Difference Built-up Index | Jiaozuo, China (2007) [45] |
Multi-layer perceptron neural network with support vector machine | Beijing, China (2009) [46] |
Normalized Impervious Surface Index | Xiamen City, China (2009) [47] |
Linear spectral unmixing | Pearl River Delta, China (1998, 2003 & 2008) [48] |
Census Tract No. | 3 | 4 | 5 | 6.01 | 6.02 | 11 | 23 | 24 | 26 | 27 | 28 |
Percent IS | 44.5 | 37.4 | 44.2 | 22.5 | 32.0 | 89.5 | 24.3 | 26.7 | 39.6 | 35.5 | 13.3 |
Hectares | 883.3 | 359.6 | 354.3 | 1,106.1 | 599.8 | 105.3 | 789.7 | 283.9 | 159.2 | 415.1 | 1,077.5 |
Impervious Threshold | Overall Accuracy | Kappa |
---|---|---|
10% | 53.5% | 0.21 |
35% | 68.6% | 0.39 |
40% | 71.4% | 0.42 |
45% | 72.8% | 0.43 |
50% | 72.6% | 0.39 |
75% | 70.6% | 0.25 |
Image and Classification | OA | Kappa | Image and Classification | OA | Kappa |
---|---|---|---|---|---|
All Bands | Bands 2–6 | ||||
maximum likelihood | 56.5% | 0.22 | maximum likelihood | 68.3% | 0.38 |
minimum distance | 70.8% | 0.39 | minimum distance | 67.6% | 0.33 |
parallelepiped | 64.4% | 0.32 | parallelepiped | 72.1% | 0.33 |
Mahalanobis’ distance | 50.8% | 0.16 | Mahalanobis’ distance | 68.6% | 0.38 |
Bands 1,5, & 7 | Bands 5, 6, & 7 | ||||
maximum likelihood | 59.8% | 0.28 | maximum likelihood | 67.7% | 0.35 |
minimum distance | 71.1% | 0.40 | minimum distance | 55.1% | 0.18 |
parallelepiped | 67.3% | 0.36 | parallelepiped | 67.2% | 0.32 |
Mahalanobis’ distance | 61.8% | 0.31 | Mahalanobis’ distance | 69.4% | 0.38 |
Method | |||||
---|---|---|---|---|---|
Census Tract No. | Aerial Photos | Parallelepiped (Bands 2–6) | Minimum Distance (Bands 1, 5 & 7) | Minimumdistance (All Bands) | Mahalanobis’ Distance (Bands 5, 6, &7) |
% IS* | % IS | % IS | % IS | % IS | |
3 | 44.5 | 36.4 | 49.5 | 59.1 | 53.7 |
4 | 37.4 | 19.3 | 55.3 | 52.5 | 52.4 |
5 | 44.2 | 25.2 | 59.8 | 58.1 | 69.3 |
6.01 | 22.5 | 17.3 | 24.6 | 30.1 | 29.3 |
6.02 | 32.0 | 23.0 | 39.2 | 39.8 | 39.0 |
11 | 89.5 | 84.0 | 93.8 | 96.6 | 92.7 |
23 | 24.3 | 12.8 | 33.6 | 33.5 | 26.2 |
24 | 26.7 | 5.2 | 40.4 | 36.8 | 53.4 |
26 | 39.6 | 29.0 | 61.2 | 61.3 | 67.1 |
27 | 35.5 | 24.3 | 56.4 | 52.2 | 58.6 |
28 | 13.3 | 7.5 | 18.2 | 14.3 | 17.9 |
Census Tract No. | Method | |||
---|---|---|---|---|
Parallelepiped (Bands 2–6) | Minimum Distance (Bands 1, 5 & 7) | Minimum Distance (All Bands) | Mahalanobis’ Distance (Bands 5, 6, &7) | |
3 | −8.1 | 5.0 | 14.6 | 9.2 |
4 | −18.1 | 17.9 | 15.1 | 15.0 |
5 | −19.0 | 15.6 | 13.9 | 25.1 |
6.01 | −5.2 | 2.1 | 7.6 | 6.8 |
6.02 | −9.0 | 7.2 | 7.8 | 7.0 |
11 | −5.5 | 4.3 | 6.9 | 3.2 |
23 | −11.5 | 9.3 | 9.2 | 1.9 |
24 | −21.5 | 13.7 | 10.1 | 26.7 |
26 | −10.6 | 21.6 | 21.7 | 27.5 |
27 | −11.2 | 20.9 | 16.7 | 23.1 |
28 | −5.8 | 4.9 | 1.0 | 4.6 |
Census Tract No. | Method | |||||||
---|---|---|---|---|---|---|---|---|
Parallelepiped (Bands 2–6) | Minimum Distance (Bands 1, 5 & 7) | Minimum Distance (All Bands) | Mahalanobis’ Distance (Bands 5, 6, &7) | |||||
OA | k | OA | k | OA | k | OA | k | |
3 | 76.7% | 0.52 | 60.2% | 0.21 | 66.0% | 0.33 | 64.1% | 0.29 |
4 | 57.9% | 0.18 | 61.8% | 0.23 | 56.6% | 0.12 | 65.8% | 0.31 |
5 | 70.7% | 0.41 | 61.3% | 0.21 | 68.0% | 0.35 | 66.7% | 0.31 |
6.01 | 81.7% | 0.38 | 75.7% | 0.31 | 75.7% | 0.36 | 75.7% | 0.39 |
6.02 | 76.7% | 0.44 | 71.1% | 0.40 | 74.4% | 0.45 | 72.2% | 0.39 |
11 | 78.0% | 0.23 | 88.0% | 0.34 | 86.0% | 0.17 | 78.0% | 0.03 |
23 | 78.4% | 0.29 | 79.4% | 0.49 | 73.5% | 0.39 | 77.5% | 0.38 |
24 | 69.1% | 0.09 | 75.0% | 0.47 | 73.5% | 0.44 | 69.1% | 0.40 |
26 | 64.9% | 0.12 | 71.9% | 0.47 | 68.4% | 0.40 | 68.4% | 0.41 |
27 | 65.8% | 0.24 | 64.6% | 0.28 | 65.8% | 0.30 | 60.8% | 0.24 |
28 | 85.8% | 0.31 | 78.8% | 0.21 | 82.3% | 0.28 | 85.0% | 0.47 |
Census Tract No. | % IS Aerial Photos | Average Parcel Size (ha) | Range of Parcel Sizes (ha) | Highest Overall Accuracy | Highest Kappa | Best Classification Method |
---|---|---|---|---|---|---|
3 | 44.5 | 0.39 | 234.5 | 76.7% | 0.52 | parallelepiped, bands 2–6 |
4 | 37.4 | 0.14 | 6.0 | 65.8% | 0.31 | Mahalanobis’ distance (bands 5, 6, & 7) |
5 | 44.2 | 0.12 | 6.3 | 70.7% | 0.41 | parallelepiped, bands 2–6 |
6.01 | 22.5 | 0.33 | 79.2 | 81.7% | 0.39 | parallelepiped, bands 2–6 |
6.02 | 32.0 | 0.26 | 26.7 | 76.7% | 0.45 | parallelepiped, bands 2–6 |
11 | 89.5 | 0.11 | 2.1 | 88.0% | 0.34 | minimum distance(bands 1, 5 & 7) |
23 | 24.3 | 0.27 | 47.1 | 79.4% | 0.49 | minimum distance (bands 1, 5 & 7) |
24 | 26.7 | 0.11 | 14.7 | 75.0% | 0.47 | minimum distance (bands 1, 5 & 7) |
26 | 39.6 | 0.08 | 28.9 | 71.9% | 0.47 | minimum distance (bands 1, 5 & 7) |
27 | 35.5 | 0.12 | 26.3 | 65.8% | 0.30 | minimum distance (all bands) |
28 | 13.3 | 0.33 | 82.9 | 85.8% | 0.47 | Mahalanobis’ distance (bands 5, 6, & 7) |
Acknowledgments
Conflict of Interest
References and Notes
- Deelstra, T.; Girardet, H. Urban Agriculture and Sustainable Cities. In Growing Cities, Growing Food: Urban Agriculture on the Policy Agenda, a Reader on Urban Agriculture; Bakker, N., Dubbeling, M., Gundel, S., Sabel-Koschella, U., de Zeeuw, H., Eds.; Deutsche Stiftung für Internationale Entwicklung; Zentralstelle für Ernährung und Landwirtschaft: Feldafing, Germany, 2000; pp. 43–66. [Google Scholar]
- Pickett, S.T.A.; Cadenasso, M.L.; Grove, J.M.; Nilon, C.H.; Pouyat, R.V.; Zipperer, W.C.; Costanza, R. Urban ecological systems: Linking terrestrial ecological, physical, and socieoeconomic components of metropolitan areas. Annu. Rev. Ecol. Syst 2001, 3, 127–157. [Google Scholar]
- United Nations, World Urbanization Prospects: The 2009 Revision; Population Division, Department of Economic and Social Affairs, UN: New York, NY, USA, 2010.
- Geiger, R.; Aron, R.H.; Todhunter, P. The Climate Near the Ground, 6th ed.; Rowman & Littlefield: Lanham, MD, USA, 2003. [Google Scholar]
- Slonenecker, E.T.; Jennings, D.B.; Garafalo, D. Remote sensing of impervious surfaces: A review. Remote Sens. Rev 2001, 20, 227–255. [Google Scholar]
- DeBusk, K.; Hunt, W.F.; Hatch, U.; Sydorovych, O. Watershed retrofit and management evaluation for urban stormwater management systems in North Carolina. J. Contemp. Water Res. Educ 2010, 146, 64–74. [Google Scholar]
- Welker, A.L.; Wadzuk, B.M.; Traver, R.G. Integration of education, scholarship, and service through stormwater management. J. Contemp. Water Res. Educ 2010, 146, 83–91. [Google Scholar]
- Oke, T.R. Initial Guidance to Obtain Representative Meteorological Observations at Urban Sites. In Instruments and Observing Methods; World Meteorological Organization: Vancouver, BC, Canada, 2006. [Google Scholar]
- Ogashawara, I.; Bastos, V.S.B. A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sens 2012, 4, 3596–3618. [Google Scholar]
- Civco, D.L.; Hurd, J.D. Impervious Surface Mapping for the State of Connecticut. Proceedings of American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Conference, Seattle, WA, USA, 7–10 April 1997.
- Davis, A.P.; Traver, R.G.; Hunt, W.F. Improving urban stormwater quality: Applying fundamental principles. J. Contemp. Water Res. Educ 2010, 146, 3–10. [Google Scholar]
- Tran, T.D.; Puissant, A.; Badariotti, D.; Weber, C. Optimizing spatial resolution of imagery for urban form detection—the cases of France and Vietnam. Remote Sens 2011, 3, 2128–2147. [Google Scholar]
- Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ 2012, 117, 34–49. [Google Scholar]
- Bauer, M.E.; Loeffelholz, B.; Wilson, B. Estimation Mapping and Change Analysis of Impervious Surface Area by Landsat Remote Sensing. Proceedings of Pecora 16—Global Priorities in Land Remote Sensing, Sioux Falls, SD, USA, 22–27 October 2005.
- Civco, D.L.; Hurd, J.D.; Wilson, E.H.; Arnold, C.L.; Prisloe, M.P., Jr. Quantifying and describing urbanizing landscapes in the Northeast United States. Photogramm. Eng. Remote Sens 2002, 68, 1083–1090. [Google Scholar]
- Chormanski, J.; van de Voorde, T.; DeRoeck, T.; Batelaan, O.; Canters, F. Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface over. Sensors 2008, 8, 910–932. [Google Scholar]
- Van de Voorde, T.; de Roeck, T.; Canters, F. A comparison of two spectral mixture modeling approaches for impervious surface mapping in urban areas. Int. J. Remote Sens 2009, 30, 4785–4806. [Google Scholar]
- Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sens. Environ 2003, 86, 370–384. [Google Scholar]
- Bauer, M.E.; Heinert, N.J.; Doyle, J.K.; Yuan, F. Impervious Surface Mapping and Change Monitoring Using Landsat Remote Sensing. Proceedings of American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Conference, Denver, CO, USA, 23–28 May, 2004.
- Lo, C.P.; Quattrochi, D.A.; Luvall, J.C. Application of high-resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. Int. J. Remote Sens 1997, 18, 287–304. [Google Scholar]
- Quattrochi, D.A.; Ridd, M.K. Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data. Int. J. Remote Sens 1994, 15, 1991–2022. [Google Scholar]
- Roth, M.; Oke, T.R.; Emery, W.J. Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. Int. J. Remote Sens 1989, 10, 1699–1720. [Google Scholar]
- Yang, L.; Huang, C.; Homer, C.G.; Wylie, B.K.; Coan, M.J. An approach for mapping large-area impervious surfaces: Synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Canadian J. Remote Sens 2003, 29, 230–240. [Google Scholar]
- Luo, L.; Mountrakis, G. Integrating intermediate inputs from partially classified images within a hybrid classification framework: An impervious surface estimation example. Remote Sens. Environ 2010, 114, 1220–1229. [Google Scholar]
- Moller-Jensen, L. Knowledge-based classification of an urban area using texture and context information in Landsat -TM imagery. Photogramm. Eng. Remote Sens 1990, 56, 899–904. [Google Scholar]
- Ji, M.; Jensen, J.R. Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper imagery. Geocarto Int 1999, 14, 33–41. [Google Scholar]
- Wang, Y.; Zhou, Y.; Zhang, X. The SPLIT and MASC Models for Extraction of Impervious Surface Areas from Multiple Remote Sensing Data. In Remote Sensing of Impervious Surfaces; Weng, Q., Ed.; CRC Press: Boca Raton, FL, USA, 2007; pp. 77–93. [Google Scholar]
- Zhou, B.; He, H.S.; Nigh, T.A.; Schultz, J.H. Mapping and analyzing change of impervious surface for two decades using multi-temporal landsat imagery in Missouri. Int. J. Appl. Earth Obs. Geoinf 2012, 18, 195–206. [Google Scholar]
- Wu, C.; Murray, A.T. Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ 2003, 84, 493–505. [Google Scholar]
- Gillies, R.R.; Brim Box, J.; Symanzik, J.; Rodenmaker, E.J. Effects of urbanization on the aquatic fauna of the Line Creek watershed, Atlanta—A satellite perspective. Remote Sens. Environ 2003, 86, 411–422. [Google Scholar]
- Dougherty, M.; Dymond, R.L.; Goetz, S.J.; Jantz, C.A.; Goulet, N. Evaluation of impervious surface estimates in a rapidly urbanizing watershed. Photogramm. Eng. Remote Sens 2004, 70, 1275–1284. [Google Scholar]
- Jantz, P.; Goetz, S.; Jantz, C. Urbanization and the loss of resource lands in the Chesapeake Bay Watershed. Environ. Manag 2005, 36, 808–825. [Google Scholar]
- Xian, G.; Crane, M. Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sens. Environ 2005, 97, 203–215. [Google Scholar]
- Xian, G. Mapping Impervious Surfaces Using Classification and Regression Tree Algorithm. In Remote Sensing of Impervious Surfaces; Weng, Q., Ed.; CRC Press: Boca Raton, FL USA, 2007; pp. 39–59. [Google Scholar]
- Shahtahmassebi, A.; Yu, Z.; Wang, K.; Xu, H.; Deng, J.; Li, J.; Luo, R.; Wu, J.; Moore, N. Monitoring rapid urban expansion using a multi-temporal RGB-impervious surface model. J. Zhejiang University Sci. A 2012, 13, 146–158. [Google Scholar]
- Yang, L.; Xian, G.; Klaver, J.M.; Deal, B. Urban land cover-change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogramm. Eng. Remote Sens 2003, 69, 1003–1010. [Google Scholar]
- Sexton, J.O.; Son, X.; Huang, C.; Channan, S.; Baker, M.E.; Townshend, J.R. Urban growth of the Washington, D.C.—Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sens. Environ 2013, 129, 42–53. [Google Scholar]
- Wu, C. Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sens. Environ 2004, 93, 480–492. [Google Scholar]
- Lu, D.; Weng, Q. Use of impervious surface in urban land-use classification. Remote Sens. Environ 2006, 102, 146–160. [Google Scholar]
- Yang, F.; Matsushita, B.; Fukushima, T. A pre-screened and normalized multiple endmember spectral mixture analysis for mapping impervious surface area in Lake Kasumigaura Basin, Japan. ISPRS J. Photogramm. Remote Sens 2010, 65, 479–490. [Google Scholar] [Green Version]
- Deng, C.; Wu, C. A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sens. Environ 2013, 133, 62–70. [Google Scholar]
- Bauer, M.E.; Loffelholz, B.C.; Wilson, B. Estimating and Mapping Impervious Surface Area by Regression Analysis of Landsat imagery. In Remote Sensing of Impervious Surfaces; Weng, Q., Ed.; CRC Press: Boca Raton, FL, USA, 2007; pp. 3–19. [Google Scholar]
- Wu, C.; Yuan, F. Seasonal sensitivity analysis of impervious surface estimation with satellite imagery. Photogramm. Eng. Remote Sens 2007, 73, 1393–1401. [Google Scholar]
- Esch, T.; Klein, D.; Himmler, V.; Keil, M.; Mehl, H.; Dech, S. Modeling of Impervious Surface in Germany Using Landsat Images and Topographic Vector Data. Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Cape Town, South Africa, 12–17 July 2009; 3, pp. 881–884.
- Xiaotian, G.; Xiaoping, L. Object-oriented Extraction of Urban Impervious Surface Coverage. Proceedings of 2011 2nd International Conference Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Zhengzhou, China, 8–10 August 2011.
- Sun, Z.; Guo, H.; Li, X.; Lu, L.; Dua, X. Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine. J. Appl. Remote Sens 2011, 5, 053501. [Google Scholar]
- Xu, H.; Lin, D.; Tang, F.; Wei, C. Remote Sensing of Impervious Surface Dynamics of Xiamen City, Southeastern China. Proceedings of 2011 19th International Conference on Geoinformatics, Shanghai, China, 24–26 June 2011.
- Deng, Y.; Fan, F.; Chen, R. Extraction and analysis of impervious surfaces based on a spectral un-mixing method using Pearl River Delta of China Landsat TM/ETM+ imagery from 1998 to 2008. Sensors 2012, 12, 1846–1862. [Google Scholar]
- Chabaeva, A.; Civco, D.L.; Hurd, J.D. Assessment of impervious surface estimation techniques. J. Hydrol. Eng 2009, 14, 377–387. [Google Scholar]
- Li, W.; Ouyang, Z.; Zhou, W.; Chen, Q. Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces. J. Environ. Sci 2011, 23, 1375–1383. [Google Scholar]
- Yuan, F.; Wu, C.; Bauer, M.E. Comparison of spectral analysis techniques for impervious surface estimation using Landsat imagery. Photogramm. Eng. Remote Sens 2008, 74, 1045–1055. [Google Scholar]
- Virginia Department of Environmental Quality GIS Data Sets; Virginia Department of Environmental Quality: Richmond, VA, USA, 2012. Available online: http://www.deq.virginia.gov/ConnectWithDEQ/VEGIS/VEGISDatasets.aspx (accessed on 13 June 2012).
- City of Roanoke 2009 Carbon Emissions and Energy Summary. Available online: http://www.roanokeva.gov/85256A8D0062AF37/CurrentBaseLink/433EBD6594B462838525788B00685A4A/$File/carbon_emissions.pdf (accessed on 30 January 2012).
- Dearing, D. Personal Communication, 19 January 2012.
- Virginia Geographic Information Network VBMP 2011 Update March 10. Available online: http://gisvirginia.blogspot.com/2011/03/vbmp-2011-update-march-10.html (accessed on 25 February 2012).
- Virginia Geographic Information Network Virginia Base Mapping Program 2011 & 2009 orthos. Available online: http://gisvirginia.blogspot.com/2012/02/virginia-base-mapping-program-2011-2009.html (accessed on 25 February 2012).
- Rinner, C.; Hussain, M. Toronto’s urban heat island—Exploring the relationship between land use and surface temperatures. Remote Sens 2011, 3, 1251–1265. [Google Scholar]
- Xiong, Y.; Huang, S.; Chen, F.; Ye, H.; Wang, C.; Zhu, C. The impacts of rapid urbanization on the thermal environment: a remote sensing study of Guangzhou, South China. Remote Sens 2012, 4, 2033–2056. [Google Scholar]
- Masek, J.G.; Vermote, E.F.; Saleous, N.; Wolfe, R.; Hall, F.G.; Huemmrich, F.; Gao, F.; Kutler, J.; Lim, T.K. A Landsat surface reflectance data set for North America, 1990–2000. Geosci. Remote Sens. Lett 2006, 3, 68–72. [Google Scholar]
- United Status Census Bureau What is a Census Tract? Available online: http://www.census.gov/geo/reference/gtc/gtc_ct.html (accessed on 24 July 2013).
- Masek, J.G.; Vermote, E.F.; Saleous, N.; Wolfe, R.; Hall, F.G.; Huemmrich, F.; Gao, F.; Kutler, J.; Lim, T.K. LEDAPS Calibration, Reflectance, Atmospheric Correction Preprocessing Code. Version 2. Available online: http://daac.ornl.govhttp://dx.doi.org/10.3334/ORNLDAAC/1146 (accessed on 24 July 2013).
- US Geological Survey Frequentyly Asked Questions about the Landsat Missions. Available online: http://landsat.usgs.gov/best_spectral_bands_to_use.php (accessed on 5 January 2013).
- Van de Voorde, T.; Jacquet, W.; Canters, F. Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data. Landsc. Urban. Plan 2011, 102, 143–155. [Google Scholar]
- Multi-Resolution Land Charateristics Consortium National Land Cover Database. Available online: http://www.mrlc.gov/nlcd06_data.php (accessed on 19 December 2012).
- Fry, J.; Xian, G.; Jin, S.; Dewitz, J.; Homer, C.; Yang, L.; Barnes, C.; Herold, N.; Wickham, J. Completion of the 2006 National Land Cover Database for the Conterminous United States. Photogramm. Eng. Remote Sens 2011, 77, 858–864. [Google Scholar]
- Yang, L.; Stehman, S.V.; Smith, J.H.; Wickham, J.D. Thematic accuracy of MRLC land cover for the eastern United States. Remote Sens. Environ 2001, 76, 418–422. [Google Scholar]
- Stehman, S.V.; Wickham, J.D.; Smith, J.H.; Yang, L. Thematic accuracy of the 1992 National Land-Cover Data for the Eastern United States: Statistical methodology and regional results. Remote Sens. Environ 2003, 86, 500–516. [Google Scholar]
- Wickham, J.D.; Stehman, S.V.; Gass, L.; Dewitz, J.; Fry, J.A.; Wade, T.G. Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sens. Environ 2013, 130, 294–304. [Google Scholar]
- Zhu, Z.; Yang, U.; Stehman, S.V.; Czaplewski, R.L. Accuracy assessment for the U.S. Geological Survey Regional Land-Cover Mapping Program: New York and New Jersey Region. Photogramm. Eng. Remote Sens 2000, 60, 1425–1435. [Google Scholar]
- Stehman, S.V.; Czaplewski, R.L. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sens. Environ 1998, 64, 331–344. [Google Scholar]
- Canters, F.; Chormanski, J.; van de Voorde, T.; Batelaan, O. Effects of Different Methods for Estimating Impervious Surface Cover on Runoff Estimation at Catchment Level. Proceedings of 7th International Symposium Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Lisbon, Portugal, 5–7 July 2006.
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Parece, T.E.; Campbell, J.B. Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography. Remote Sens. 2013, 5, 4942-4960. https://doi.org/10.3390/rs5104942
Parece TE, Campbell JB. Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography. Remote Sensing. 2013; 5(10):4942-4960. https://doi.org/10.3390/rs5104942
Chicago/Turabian StyleParece, Tammy E., and James B. Campbell. 2013. "Comparing Urban Impervious Surface Identification Using Landsat and High Resolution Aerial Photography" Remote Sensing 5, no. 10: 4942-4960. https://doi.org/10.3390/rs5104942