References
Al-Khudhairy, D., I. Caravaggi, and S. Glada, 2005. Structural
damage assessments from Ikonos data using change detection,
object-oriented segmentation, and classification techniques.
Photogrammetric Engineering and Remote Sensing
, 71(7):
825–837.
Antonarakis, A.S., K.S. Richards, and J. Brasington, 2008. Object-
based land cover classification using airborne LiDAR,
Remote
Sensing of Environment
, 112(6):2988–2998.
Arnold, C.L., and C.J. Gibbons, 1996. Impervious surface coverage:
the emergence of a key environmental indicator,
Journal of the
American Planning Association
, 62(2):243–258.
Baerwald, T.J., 1978. The Emergence of a New “Downtown,”
Geographical Review
, 68:308–318.
Bhaskaran, S., S. Paramananda, and M. Ramnarayan, 2010. Per-
pixel and object-oriented classification methods for mapping
urban features using Ikonos satellite data,
Applied Geography
,
30(4):650–665.
Blaschke, T., 2010. Object based image analysis for remote sensing,
ISPRS Journal of Photogrammetry and Remote Sensing
,
65(1):2–16.
Breiman, L., 1984.
Classification and Regression Tree
, Wadsworth
International Group, Belmont, California, 354 p.
Congalton, R., 1991. A review of assessing the accuracy of
classifications of remotely sensed data,
Remote Sensing of
Environment
, 37:35–46.
Congalton, R.G., and K. Green, 2009.
Assessing the Accuracy of
Remotely Sensed Data: Principles And Practices
, CRC press,
Boca Raton, Florida, 183p.
Congalton, R., and R.A. Mead, 1983. A quantitative method to
test for consistency and correctness in photointerpretation,
Photogrammetric Engineering & Remote Sensing
, 49(1):69–74.
Coppin, P.R., and M.E. Bauer, 1994. Processing of multitemporal
Landsat TM imagery to optimize extraction of forest cover
change features,
IEEE Transactions on Geoscience and Remote
Sensing
, 32(4):918–927.
Dougherty, M., R.L. Dymond, S.J. Goetz, C.A. Jantz, and N. Goulet,
2004. Evaluation of impervious surface estimates in a rapidly
urbanizing watershed,
Photogrammetric Engineering & Remote
Sensing
, 70(11):1275–1284
Foody, G.M., and P.M. Atkinson, 2002.
Uncertainty in Remote
Sensing and GIS
, Wiley Online Library, Hoboken, New Jersey.
Freund, Y., R. Schapire, and N. Abe, 1999. A short introduction to
boosting,
Journal of Japanese Society for Artificial Intelligence
,
14(771–780):1612.
Gahegan, M., 2003. Is inductive machine learning just another wild
goose (or might it lay the golden egg)?,
International Journal of
Geographical Information Science
, 17(1):69.
Hansen, M., R. Dubayah, and R. DeFries, 1996. Classification trees:
An alternative to traditional land cover classifiers,
International
Journal of Remote Sensing
, 17(5):1075–1081.
Hansen, M.C., R.S. Defries, J.R.G. Townshend, and R. Sohlberg, 2000.
Global land cover classification at 1 km spatial resolution using
a classification tree approach,
International
Journal of Remote
Sensing
, 21(6-7):1331–1364.
Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N.
Herold, A. McKerrow, J.N.
VanDriel, and J. Wickham, 2007. Completion of the 2001 national
land cover database for the conterminous United States,
Photogrammetric Engineering & Remote Sensing
, 73(4):337–341.
Johnson, D.M., and R. Mueller, 2010. The 2009 cropland data layer,
Photo-
grammetric Engineering & Remote Sensing
, 76(11):1202–1205.
Lee, S., and R.G. Lathrop, 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(6):1642–1654.
Lu, D., and Q. Weng, 2006. Use of impervious surface in urban
land-use classification,
Remote Sensing of Environment
, 102(1-
2):146–160.
Lunetta, R.S., and M.E. Balogh, 1999. Application of multi-
temporal Landsat-5 TM imagery for wetland identification,
Photogrammetric Engineering & Remote Sensing
, 65(11):1303–
1310.
Mesev, V., 1998. The use of census data in urban image classification,
Photogrammetric Engineering & Remote Sensing
, 64(5):431–438.
Metropolitan Council, 2014. A growing and changing Twin Cities region:
Regional forecast to 2040.
Metro Stats
, February, URL:
.
metrocouncil.org
(last date accessed: 13 November 2015.)
Nagel, P., B. Cook, and F. Yuan, 2014. High spatial-resolution land
cover classification and wetland mapping over large areas using
integrated geospatial technologies,
International
Journal of
Remote Sensing Applications
, 4(2):71–86.
Opitz, D., and W. Bain, 1999. Experiments on learning to extract
features from digital images,
Proceedings of the IASTED
Conference on Signal and Image Processing
, 18-21 October 1999,
Nassau, Bahamas.
Ozesmi, S.L. and M.E. Bauer, 2002. Satellite remote sensing of
wetlands,
Wetlands Ecology and Management
, 10(5):381-402.
Pal, M., and P.M. Mather, 2003. An assessment of the effectiveness
of decision tree methods for land cover classification,
Remote
Sensing of Environment
, 86(4):554–565.
Quinlan, J.R., 1993. C4.5: Programs for Machine Learning, San Mateo,
California, Morgan Kaufmann.
Quinlan, J.R., 2013. Information on See5/C5.0, URL:
.
rulequest.com/see5-info.html
(last date accessed: 13 November
2015).
Reese, H.M.,T.M. Lillesand, D.E. Nagel, J.S. Stewart, R.A. Goldmann,
T.E. Simmons, J.W.
Chipman, and P.A. Tessar, 2002. Statewide land cover derived from
multiseasonal Landsat TM data: A retrospective of the WISCLAND
project,
Remote Sensing of Environment
, 82(2):224–237.
Ryherd, S., and C. Woodcock, 1996. Combining spectral and
texture data in the segmentation of remotely sensed images,
Photogrammetric Engineering & Remote Sensing
, 62(2):181–194.
Saatchi, S.S., J.V. Soares, and D.S. Alves, 1997. Mapping deforestation
and land use in Amazon rainforest by using SIR-C imagery,
Remote Sensing of Environment
, 59 (2):191–202.
Schueler, T.R., 1994. The importance of imperviousness,
Watershed
Protection Techniques
, 1(3):100–110.
Story, M., and R. Congalton, 1986. Accuracy assessment: A user’s
perspective,
Photogrammetric Engineering & Remote Sensing
,
52(3):397–399.
Squires, G.D., 2002.
Urban sprawl: Causes, Consequences, and Policy
Responses
, Urban Institute Press, Washington, D.C., 367 p.
Stehman, S.V. and R. L. Czaplewski, 1998. Design and analysis
for thematic map accuracy assessment,
Remote Sensing of
Environment
, 64:331–344.
Sutton, P.C., S.J. Anderson, C.D. Elvidge, B.T. Tuttle, and T. Ghosh,
2009. Paving the planet: Impervious surface as proxy measure of
the human ecological footprint,
Progress in Physical Geography
,
33(4):510–527.
US Census, 2013. American Factfinder 2013, URL:
census.gov
(last date accessed: 13 November 2015).
Whiteside, T.G., G.S. Boggs, and S.W. Maier, 2011. Comparing object-
based and pixel-based classifications for mapping savannas,
International Journal of Applied Earth Observation and
Geoinformation
, 13(6):884–893.
Wickham, J.D., S.V.Stehman, L. Gass, J. Dewitz, J.A. Fry, and T.G.
Wade, 2013. Accuracy assessment of NLCD 2006 land cover and
impervious surface, Remote Sensing of Environment, 130:294–
304.
Wolter, P.T., D.J. Mladenoff, G.E. Host, and T.R. Crow, 1995. Improved
forest classification in the Northern Lake States using multi-
temporal Landsat imagery,
Photogrammetric Engineering &
Remote Sensing
, 61(9):1129–1144.
Wu, C., 2004. Normalized spectral mixture analysis for monitoring
urban composition using ETM+ imagery,
Remote Sensing of
Environment
, 93:480–492.
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