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31
Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp 369–386
47. McPherson MB, Schneider WJ (1974) Problems in modeling urban watersheds. Water Resour
Res 10(3):434–440
48. Carlson TN (2007) Impervious surface area and its effect on water abundance and water quality.
In: Weng Q (ed) Remote sensing of impervious surfaces. CRC, Boca Raton, pp 353–367
49. Brabec E, Schulte S, Richards PL (2002) Impervious surfaces and water quality: a review of
current literature and its implications for watershed planning. J Plan Lit 16(4):499–514
50. Multi-Resolution Land Characteristics Consortium (2006) National land cover database percent
developed imperviousness. http://www.mrlc.gov/nlcd06_data.php. Accessed 19 Dec 2012
51. Gillies RR, Brim Box J, Symanzik J, Rodemaker EJ (2003) Effects of urbanization on the
aquatic fauna of the Line Creek watershed, Atlanta—a satellite perspective. Remote Sens
Environ 86(3):411–422
52. Yang F, Matsushita B, Fukushima T (2010) 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 65(5):479–490
53. Ruddell BL, Chow WTL (2014) Microclimate analysis of observations in a master-planned
residential community in Arizona. In: 94th American meteorological society annual meeting,
Atlanta, 2–6 Feb 2014
54. Parece TE, Campbell JB, Carroll D (2014) Assessing variations in urban heat island effects
within Roanoke, Virginia. In: American society of photogrammetry and remote sensing annual
conference. Louisville, 23–28 Mar 2014
55. United States National Aeronautics and Space Administration (2014) About LVIS. http://lvis.
gsfc.nasa.gov. Accessed 24 May 2014
56. Lee DS, Shan J (2003) Combining Lidar elevation data and IKONOS multispectral imagery
for coastal classification mapping. Mar Geod 26(1/2):117–127
57. Buja ´n S, Gonza ´lez-Ferreiro E, Reyes-Bueno F, Barreiro-Ferna ´ndez L, Crecente R et al (2012)
Land use classification from Lidar data and ortho-images in a rural area. Photogramm Rec
27(140):401–422
58. de Agirre AM, Malpica JA (2012) Detecting shadows in a segmented land use land cover
image with LIDAR data. IGARSS 33rd Canadian symposium on remote sensing, Munich,
22–27 July 2012, pp 5458–5461
59. Meng X, Currit N, Wang L, Yang X (2010) Object-oriented residential building land-use
mapping using lidar and aerial photographs. American society of photogrammetry and remote
sensing 2010 annual conference, San Diego, 26–30 Apr 2010
60. Antonarakis A, Richards K, Brasington J (2008) Object-based land cover classification using
airborne LiDAR. Remote Sens Environ 112(6):2988–2998
61. NASA Jet Propulsion Laboratory (2014) Airborne visible/infrared imaging spectrometer.
http://aviris.jpl.nasa.gov/. Accessed 5 June 2014
62. Amato U, Antoniadis A, Carfora MF, Colandrea P, Cuomo V et al (2013) Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use. IEEE J Sel
Topics Appl Earth Observ Remote Sens 6(2):615–625
63. United States Geological Survey (2011) Earth observing, sensors—hyperion. http://eo1.usgs.
gov/sensors/hyperion. Accessed 6 June 2014
64. Petropoulos GP, Arvanitis K, Sigrimis N (2012) Hyperion hyperspectral imagery analysis
combined with machine learning classifiers for land use/cover mapping. Exp Syst Appl 39(3):
3800–3809
65. Petropoulos GP, Kalaitzidis C, Prasad Vadrevu K (2012) Support vector machines and objectbased classification for obtaining land-use/cover cartography from Hyperion hyperspectral
imagery. Comput Geosci 41:99–107
66. Ampe EM, Vanhamel I, Salvadore E, Jef D, Bashir I et al (2012) Impact of urban land-cover
classification on groundwater recharge uncertainty. IEEE J Sel Topics Appl Earth Observ
Remote Sens 5(6):1859–1867
Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview
31