Considering plant phenology for improving the accuracy of urban impervious surface mapping in a subtropical climate regions

International Journal of Remote Sensing

Volumn 33, Issue 1, 2012, Pages 261-275

  Chan, Yong Sung ^a   Li, Ming Han ^b  

^a TEXAS A AND M UNIVERSITY SYSTEM   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; MAPPING; MEAN SQUARE ERROR; PLANTS (BOTANY); REMOTE SENSING; TROPICS;

IMPERVIOUS SURFACE; IMPERVIOUS SURFACE ESTIMATION; LANDSAT THEMATIC MAPPER IMAGES; REMOTE SENSING IMAGES; ROOT MEAN SQUARE ERRORS; SUBTROPICAL CLIMATES; TEMPERATE CLIMATE; URBAN IMPERVIOUS SURFACES;

IMAGE ENHANCEMENT;

ACCURACY ASSESSMENT; DECIDUOUS TREE; ESTIMATION METHOD; IMAGING METHOD; LANDSAT THEMATIC MAPPER; MAPPING METHOD; REMOTE SENSING; SUBTROPICAL REGION; VEGETATION COVER;

BIOLOGY; FORESTRY; HARDWOODS; MAPPING; METEOROLOGY; PHENOLS; REMOTE SENSING; TROPICS;

EID: 82155171329     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2011.591445     Document Type: Article

References (52)

1. Alberti, M., Weeks, R. and Coe, S., 2004, Urban land-cover change analysis in central Pugetsound. Photogrammetric Engineering & Remote Sensing, 70, pp. 1043-1052.
2. Arnold Jr., C.L., and Gibbons, C.J., 1996, Impervious surface coverage: the emergenceof a key environmental indicator. Journal of the American Planning Association, 62,pp. 243-258.
3. Brabec, E., Schulte, S. and Richards, P.L., 2002, Impervious surfaces and water quality:a review of current literature and its implications for watershed planning. Journal ofPlanning Literature, 16, pp. 499-514.
4. Brezonik, P.L. and Stadelmann, T.H., 2002, Analysis and predictive models of stormwaterrunoff volumes, loads, and pollutant concentrations from watersheds in the Twin citiesmetropolitan area, Minnesota, USA. Water Research, 36, pp. 1743-1757.
5. Cablk, M.E. and Minor, T.B., 2003, Detecting and discriminating impervious cover withhigh-resolution IKONOS data using principal component analysis and morphologicaloperators. International Journal of Remote Sensing, 24, pp. 4627-4645.
6. City of Austin, 2010, Watershed ordinances: a retrospective. Available online at:http://www.ci.austin.tx.us/watershed/ordinances.htm (accessed 10 June 2010).
7. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D. and Weingessel, A., 2009, Package'e1071'. Available online at: http://cran.rproject.org/web/packages/e1071/e1071.pdf(accessed 15 May 2010).
8. Dougherty, M., Dymond, R.L., Goetz, S.J., Jantz, C.A. and Goulet, N., 2004, Evaluationof impervious surface estimates in a rapidly urbanizing watershed. PhotogrammetricEngineering and Remote Sensing, 70, pp. 1275-1284.
9. Everitt, J.H., Yang, C. and Johnson, H.B., 2007, Canopy spectra and remote sensing ofAshe juniper and associated vegetation. Environmental Monitoring and Assessment, 130,pp. 403-413.
10. Gillies, R.R., Box, J.B., Symanzik, J. and Rodemaker, E.J., 2003, Effects of urbanization onthe aquatic fauna of the line creek watersheds, Atlanta - a satellite perspective. RemoteSensing of Environment, 86, pp. 411-422.
11. Goonetilleke, A., Thomas, E., Ginn, S. and Gilbert, D., 2005, Understanding therole of land use in urban stormwater quality management. Journal of EnvironmentalManagement, 74, pp. 31-42.
12. Groffman, P.M., Bain, D.J., Band, L.E., Belt, K.T., Brush, G.S., Grove, J.M., Pouyat, R.V., Yesilonis, I.C. and Zipperer, W.C., 2003, Down by the riverside: urban riparian ecology. Frontiers in Ecology and the Environment, 1, pp. 315-321.
13. Groffman, P.M., Boulware, N.J., Zipperer, W.C., Pouyat, R.V., Band, L.E. and Colosimo, M.F., 2002, Soil nitrogen cycle processes in urban riparian zones. Environmental Science & Technology, 36, pp. 4547-4552.
14. Hu, X. and Weng, Q., 2009, Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113, pp. 2089-2102.
15. Huang, C., Dav i s, L.S. and Townshend, J.R.G., 2002, An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, pp. 725-749.
16. Lardeux, C., Frison, P.-L., Tison, C., Souyris, J.-C., Stoll, B., Frumeau, B. and Rudant, J.-P., 2009, Support vector machine for multifrequency SAR polarimet-ric data classification. IEEE Transactions on Geoscience and Remote Sensing, 47, pp. 4143-4152.
17. Lee, S. and Lathrop, R.G., 2006, Subpixel analysis of Landsat ETM+ using self-organizing map (SOM) neural networks for urban land cover characterization. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 1642-1654.
18. Li, M.-H., Barrett, M.E., Rammohan, P., Olivera, F. and Landphair, H.C., 2008, Documenting stormwater quality on Texas highways and adjacent vegetated roadsides. Journal of Environmental Engineering, 134, pp. 48-59.
19. Liu, D., Kelly, M. and Gong, P., 2006, A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote Sensing of Environment, 101, pp. 167-180.
20. Lu, D. and Weng, Q., 2004, Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogrammetric Engineering and Remote Sensing, 70, pp. 1053-1062.
21. Marsh, W.M. and Marsh, N.L., 1995, Hydrogeomorphic considerations in development planning and stormwater management, Central Texas Hill Country, USA. Environmental Management, 19, pp. 693-702.
22. Maskell, L.C., Bullock, J.M., Smart, S.M., Thompson, K. and Hulme, P.E., 2006, The distribution and habitat associations of non-native plant species in urban riparian habitats. Journal of Vegetation Science, 17, pp. 499-508.
23. Mazzoni, D., Logan, J.A., Diner, D., Kahn, R., Tong, L. and Li, Q., 2007, A data-mining approach to associating MISR smoke plume heights with MODIS fire measurements. Remote Sensing of Environment, 107, pp. 134-148.
24. Mcmahan, C.A., Frye, R.G. and Brown, K.L., 1984, The Vegetation Types of Texas, Including Cropland (Austin, TX: Texas Parks and Wildlife Department).
25. Melgani, F. and Bruzzone, L., 2004, Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, pp. 1778-1790.
26. Moffatt, S.F., Mclachlan, S.M. and Kenkel, N.C., 2004, Impacts of land use on riparian forest along an urban-rural gradient in southern Manitoba. Plant Ecology, 174, pp. 119-135.
27. Morse, C.C., Huryn, A.D. and Cronan, C., 2003, Impervious surface area as a predictor of the effects of urbanization on stream insect communities in Maine, U.S.A. Environmental Monitoring and Assessment, 89, pp. 95-127.
28. Nash, N.E. and Sutcliffe, J.V., 1970, River flow forecasting through conceptual models part I - a discussion of principles. Journal of Hydrology, 10, pp. 282-290.
29. Nemmour, H. and Chibani, Y., 2006, Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS Journal of Photogrammetry and Remote Sensing, 61, pp. 125-133.
30. Pal, M. and Mather, P.M., 2004, Assessment of the effectiveness of support vector machinesfor hyperspectral data. Future Generation Computer Systems, 20, pp. 1215-1225.
31. Ridd, M.K., 1995, Exploring a V-I-S (vegetation-impervious surface-soil) model for urbanecosystem analysis through remote sensing: a comparative anatomy for cities.International Journal of Remote Sensing, 16, pp. 2165-2185.
32. Riley, S.P.D., Busteed, G.T., Kats, L.B., Vandergon, T.L., Lee, L.F.S., Dagit, R.G., Ker by, J.L., Fisher, R.N. and Sauvajor, R.M., 2005, Effects of urbanization on thedistribution and abundance of amphibians and invasive species in southern Californiastreams. Conservation Biology, 19, pp. 1894-1907.
33. Sawaya, K.E., Olmanson, L.G., Heinert, N.J., Brezonik, P.L. and Bauer, M.E., 2003, Extending satellite remote sensing to local scales: land and water resource monitoringusing high-resolution imagery. Remote Sensing of Environment, 88, pp. 144-156.
34. Scanlon, B.R., Mace, R.E., Barrett, M.E. and Smith, B., 2003, Can we simulate regionalgroundwater flow in a karst system using equivalent porous media models? Case study, Barton Springs Edwards Aquifer, USA. Journal of Hydrology, 276, pp. 137-158.
35. Simmons, D.L. and Reynolds, R.J., 1982, Effects of urbanization on base flow ofselected south-shore streams, Long Island, New York. Water Resources Bulletin,18,pp. 797-805.
36. Small, C., 2002, Multitemporal analysis of urban reflectance. Remote Sensing of Environment,81, pp. 427-442.
37. Smola, A.J. and Schölkopf, B., 2004, A tutorial on support vector regression. Statistics and Computing, 14, pp. 199-222.
38. Sung, C.Y. and Li, M.-H., 2010, The effect of urbanization on stream hydrology in hillslopewatersheds in central Texas. Hydrological Processes, 24, pp. 3706-3717.
39. Sung, C.Y., Li, M.-H., Rogers, G.O., Volder, A. and Wang, Z., 2011, Investigating alienplant invasion in urban riparian forests in a hot and semi-arid region. Landscape and Urban Planning., 100, pp. 278-286.
40. Sutherland, A.B., Meyer, J.L. and Gardiner, E.P., 2002, Effects of land cover on sedimentregime and fish assemblage structure in four southern Appalachian streams. Freshwater Biology, 47, pp. 1791-1805.
41. Thucker, C.J., 1979, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, pp. 127-150.
42. US Census Bureau, 2010, Population finder: Austin City, TexasAvailable online at: http://factfinder.census.gov (accessed 26 May 2010).
43. US National Weather Service, 2010, Austin/San Antonio, TX. Available online at: http://www.weather.gov/climate/index.php?wfo=ewx (accessed 6 June 2010).
44. van De Voorde, T., De Roeck, T. and Canters, F., 2009, A comparison of two spectral mixture modelling approach for impervious surface mapping in urban areas. InternationalJournal of Remote Sensing, 30, pp. 4785-4806.
45. Vapnik, V.N., 1995, The Nature of Statistical Learning Theory, pp. 1-188 (New York: Springer).
46. Walsh, C.J., Roy, A.H., Feminella, J.W., Cottingham, P.D., Groffman, P.M. and Morgan II, R.P., 2005, The urban stream syndrome: current knowledge and the search for a cure. Journal of the North American Benthological Society, 24, pp. 706-723.
47. Walton, J.T., 2008, Subpixel urban land cover estimation: comparing cubist, random forests,and support vector regression. Photogrammetric Engineering and Remote Sensing, 74,pp. 1213-1222.
48. Wang, L., John, L., Kanehl, P. and Bannerman, R., 2001, Impacts of urbanization on stream habitat and fish across multiple spatial scales. Environmental Management,28,pp. 255-266.
49. Weng, Q. and Hu, X., 2008, Medium spatial resolution satellite imagery for estimating and mapping urban impervious surfaces using LSMA and ANN. IEEE Transactions on Geoscience and Remote Sensing, 46, pp. 2397-2406.
50. Wu, C. and Murray, A.T., 2003, Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, 2003, pp. 493-505.
51. Wu, C. and Yuan, F., 2007, Seasonal sensitivity analysis of impervious surface estimation with satellite imagery. Photogrammetric Engineering and Remote Sensing, 73, pp. 1393-1401.
52. Yang, L., Xian, G., Kav e r, J.M. and Deal, B., 2003, Urban land-cover changedetection through sub-pixel imperviousness mapping using remotely sensed data.Photogrammetric Engineering & Remote Sensing, 69, pp. 1003-1010.