Drivers of land cover and land use changes in St. Louis metropolitanarea over the past 40 years characterized by remote sensing andcensus population data

International Journal of Applied Earth Observation and Geoinformation

Volumn 35, Issue PB, 2015, Pages 161-174

  Maimaitijiang, Maitiniyazi ^a,b   Ghulama, Abduwasit ^a   Onesimo Sandoval J S ^a   Maimaitiyiming, Matthew ^a  

^a SAINT LOUIS UNIVERSITY   (United States)

^b XINJIANG AGRICULTURAL UNIVERSITY   (China)

Author keywords

Geographically weighted regression; Land cover and land use; Urban growth

Indexed keywords

CENSUS; LAND COVER; LAND USE CHANGE; LANDSAT; LEAST SQUARES METHOD; METROPOLITAN AREA; SATELLITE DATA; URBAN GROWTH; URBAN POPULATION;

MISSOURI; SAINT LOUIS [MISSOURI]; UNITED STATES;

EID: 84924355085     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.08.020     Document Type: Article

References (79)

1. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716-723.
2. Alperovich, G., Deutsch, J., 1992. Population-density gradients and urbanizationmeasurement. Urban Stud. 29, 1323-1328.
3. Anderson, J.R., 1976. A land use and land cover classification system for use withremote sensor data. US Government Printing Office.
4. Arnfield, A.J., 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol. 23, 1-26.
5. Bagan, H., Yamagata, Y., 2012. Landsat analysis of urban growth: how Tokyo becamethe world's largest megacity during the last 40 years. Remote Sens. Environ. 127, 210-222.
6. Banzhaf, E., Reyes-Paecke, S., Muller, A., Kindler, A., 2013. Do demographic and landuse changes contrast urban and suburban dynamics? A sophisticated reflectionon Santiago de Chile. Habitat Int. 39, 179-191.
7. Bellehumeur, C., Legendre, P., 1998. Multiscale sources of variation in ecologicalvariables: modeling spatial dispersion, elaborating sampling designs. Landsc. Ecol. 13, 15-25.
8. Berry, B.J.L., 1990. Urbanism, colonialism, and the world-economy-cultural andspatial foundations of the world urban system. J. Asian Stud. 49, 617-619.
9. Bhatta, B., 2009. Analysis of urban growth pattern using remote sensing and GIS: acase study of Kolkata, India. Int. J. Remote Sens. 30, 4733-4746.
10. Bhatta, B., Saraswati, S., Bandyopadhyay, D., 2010. Quantifying the degree-of-freedom, degree- of-sprawl, and degree-of-goodness of urban growth fromremote sensing data. Appl. Geogr. 30, 96-111.
11. Cohen, W.B., Goward, S.N., 2004. Landsat's role in ecological applications of remotesensing. Bioscience 54, 535-545.
12. Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn. 20, 273-297.
13. De Freitas, M.W.D., dos Santos, J.R., Alves, D.S., 2013. Land use and land cover changeprocesses in the Upper Uruguay Basin: linking environmental and socioecono-mic variables. Landsc. Ecol. 28, 311-327.
14. Deng, X., Huang, J., Rozelle, S., Uchida, E., 2008. Growth, population and industrial-ization, and urban land expansion of China. J. Urban Econ. 63, 96-115.
15. Foody, G.M., Mathur, A., 2006. The use of small training sets containing mixed pixelsfor accurate hard image classification: training on mixed spectral responses forclassification by a SVM. Remote Sens. Environ. 103, 179-189.
16. Fortin, M.J., 1999. Effects of sampling unit resolution on the estimation of spatialautocorrelation. Ecoscience 6, 636-641.
17. Fortin, M.J., Dale, M.R.T., 2009. Spatial autocorrelation in ecological studies: a legacyof solutions and myths. Geogr. Anal. 41, 392-397.
18. Fortin, M.J., Payette, S., 2002. How to test the significance of the relation betweenspatially autocorrelated data at the landscape scale: a case study using fire andforest maps. Ecoscience 9, 213-218.
19. Fotheringham, A.S., Brunsdon, C., Charlton, M., 2003. Geographically WeightedRegression: The Analysis of Spatially Varying Relationships. John Wiley & Sons.
20. Fox, J., 2003. People and the Environment: Approaches for Linking Household andCommunity Surveys to Remote Sensing and GIS. Springer.
21. Frumkin, H., 2002. Urban sprawl and public health. Public Health Rep. 117, 201-217.
22. Gallego, F.J., 2010. A population density grid of the European Union. Popul. Environ. 31, 460-473.
23. Gillies, R.R., Box, J.B., Symanzik, J., Rodemaker, E.J., 2003. Effects of urbanization onthe aquatic fauna of the Line Creek watershed, Atlanta-a satellite perspective. Remote Sens. Environ. 86, 411-422.
24. Gordon, C.E., 2008. Mapping Decline: St. Louis and the Fate of the American City. University of Pennsylvania Press. Griffiths, P., Hostert, P., Gruebner, O., van der Linden, S., 2010. Mapping megacitygrowth with multi-sensor data. Remote Sens. Environ. 114, 426-439.
25. Griffiths, P., Hostert, P., Gruebner, O., van der Linden, S., 2010. Mapping megacitygrowth with multi-sensor data. Remote Sens. Environ. 114, 426-439.
26. Hackworth, J., 2014. The limits to market-based strategies for addressing land aban-donment in shrinking American cities. Progr. Plan. 90, 1-37.
27. Hamilton, L.C., Press, D., 1992. Regression with Graphics: A Second Course in AppliedStatistics. Duxbury Press, Belmont.
28. Han, D., Chan, L., Zhu, N., 2007. Flood forecasting using support vector machines. J. Hydroinf. 9, 267-276.
29. Harries, K., 2006. Extreme spatial variations in crime density in Baltimore County, MD. Geoforum 37, 404-416.
30. Hasse, J.E., Lathrop, R.G., 2003. Land resource impact indicators of urban sprawl. Appl. Geogr. 23, 159-175.
31. Herold, M., Goldstein, N.C., Clarke, K.C., 2003. The spatiotemporal form of urbangrowth: measurement, analysis and modeling. Remote Sens. Environ. 86, 286-302.
32. Holt, J.B., Lo, C., Hodler, T.W., 2004. Dasymetric estimation of population density andareal interpolation of census data. Cartogr. Geogr. Inf. Sci. 31, 103-121.
33. Huang, C., Davis, L.S., Townshend, J.R.G., 2002. An assessment of support vectormachines for land cover classification. Int. J. Remote Sens. 23, 725-749.
34. Huang, S.C., Wang, H.W., 2006. Combining time-scale feature extractions with SVMsfor stock index forecasting. Neural Inf. Process. 4234 (Pt 3), 390-399.
35. Huang, G., Zhou, W., Cadenasso, M.L., 2011. Is everyone hot in the city? Spatial pat-tern of land surface temperatures, land cover and neighborhood socioeconomiccharacteristics in Baltimore, MD. J. Environ. Manag. 92, 1753-1759.
36. Jat, M.K., Garg, P.K., Khare, D., 2008. Monitoring and modelling of urban sprawl usingremote sensing and GIS techniques. Int. J. Appl. Earth Obs. 10, 26-43.
37. Jensen, J.R., 2009. Remote Sensing of the Environment: An Earth Resource Perspec-tive 2/e. Pearson Education India.
38. Jordan, Y.C., Ghulam, A., Hartling, S., 2014. Traits of surface water pollution underclimate and land use changes: a remote sensing and hydrological modelingapproach. Earth-Sci. Rev. 128, 181-195.
39. Jordan, Y.C., Ghulam, A., Herrmann, R.B., 2012. Floodplain ecosystem response toclimate variability and Land cover and Land use change in Lower Missouri Riverbasin. Landsc. Ecol. 27, 843-857.
40. Kalnay, E., Cai, M., 2003. Impact of urbanization and land use change on climate. Nature 423, 528-531.
41. Kelly, M., Estes, J.E., Knight, K.A., 1999. Image interpretation keys for validation ofglobal land cover data sets. Photogramm. Eng. Remote Sens. 65, 1041-1050.
42. Kimerling, A.J., Buckley, A.R., Muehrcke, P.C., Muehrcke, J.O., 2009. Map Use: Readingand Analysis. Esri Press.
43. Kolios, S., Stylios, C.D., 2013. Identification of land cover/land use changes in thegreater area of the Preveza peninsula in Greece using Landsat satellite data. Appl. Geogr. 40, 150-160.
44. Lambin, E.F., Geist, H.J., Lepers, E., 2003. Dynamics of land use and land cover changein tropical regions. Annu. Rev. Environ. Resour. 28, 205-241.
45. Lo, C.P., Yang, X.J., 2002. Drivers of land use and land cover changes and dynamicmodeling for the Atlanta, Georgia Metropolitan Area. Photogramm. Eng. RemoteSens. 68, 1073-1082.
46. Lopez, T.D., Aide, T.M., Thomlinson, J.R., 2001. Urban expansion and the loss of primeagricultural lands in Puerto Rico. Ambio 30, 49-54.
47. Lu, D.S., Weng, Q.H., 2006. Use of impervious surface in urban land use classification. Remote Sens. Environ. 102, 146-160.
48. Ma, M., Lu, Z., Sun, Y., 2008. Population growth, urban sprawl and landscape integrityof Beijing city. Int. J. Sust. Dev. World 15, 326-330.
49. Manson, S.M., Evans, T., 2007. Agent-based modeling of deforestation in southernYucatán, Mexico, and reforestation in the Midwest United States. Proc. Natl. Acad. Sci. U. S. A. 104, 20678-20683.
50. Mantero, P., Moser, G., Serpico, S.B., 2005. Partially supervised classification ofremote sensing images through SVM-based probability density estimation. IEEETrans. Geosci. Remote 43, 559-570.
51. Martin, D., 1996. Geographic Information Systems: Socioeconomic Applications. Psychology Press.
52. Martinuzzi, S., Gould, W.A., Gonzalez, O.M.R., 2007. Land development, land use, andurban sprawl in Puerto Rico integrating remote sensing and population censusdata. Landsc. Urban Plan. 79, 288-297.
53. Moran, P.A., 1950. Notes on continuous stochastic phenomena. Biometrika 37, 17-23.
54. MRLC, 2008. Multi-resolution Land Characteristics Consortium (MRLC) (2008).National Land Cover Database (NLCD) 1992/2001 retrofit land cover changeproduct, Available at: http://www.mrlc.gov/nlcdrlc.php
55. NOAA, 2014. National Oceanic and Atmospheric Administration (NOAA) (2014).National Climatic Data Center, Available at: http://www.ncdc.noaa.gov/
56. NWS, 2014. National Weather Service archives-St. Louis Office, Available at:http://www.crh.noaa.gov/lsx/?n=cliarchive
57. Openshaw, S., Taylor, P.J., 1979. A million or so correlation coefficients: three exper-iments on the modifiable areal unit problem. In: Wrigley, N. (Ed.), StatisticalApplications in the Spatial Sciences, vol. 21, pp. 127-144.
58. Poelmans, L., Van Rompaey, A., 2009. Detecting and modelling spatial patterns ofurban sprawl in highly fragmented areas: a case study in the Flanders-Brusselsregion. Landsc. Urban Plan. 93, 10-19.
59. Radeloff, V.C., Hagen, A.E., Voss, P.R., Field, D.R., Mladenoff, D.J., 2000. Exploring thespatial relationship between census and land cover data. Soc. Nat. Resour. 13, 599-609.
60. Romero, H., Vásquez, A., Fuentes, C., Salgado, M., Schmidt, A., Banzhaf, E., 2012. Assessing urban environmental segregation (UES). The case of Santiago de Chile. Ecol. Ind. 23, 76-87.
61. Ryznar, R.M., Wagner, T.W., 2001. Using remotely sensed imagery to detect urbanchange: viewing Detroit from space. J. Am. Plann. Assoc. 67, 327-336.
62. Sanford, S.W., Maryville, M., 2011. A Technique for Mapping Urban Areas andChange Using Integrated Remote Sensing and Dasymetric Population MappingMethods.
63. Sexton, J.O., Urban, D.L., Donohue, M.J., Song, C.H., 2013. Long-term land coverdynamics by multi-temporal classification across the Landsat-5 record. RemoteSens. Environ. 128, 246-258.
64. Small, C., Elvidge, C.D., Balk, D., Montgomery, M., 2011. Spatial scaling of stable nightlights. Remote Sens. Environ. 115, 269-280.
65. Sokal, R.R., 1974. Classification: purposes, principles, progress, prospects. Science185, 1115-1123.
66. Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., 2001. Classifica-tion and change detection using Landsat TM data: when and how to correctatmospheric effects? Remote Sens. Environ. 75, 230-244.
67. Squires, G.D., Friedman, S., Saidat, C.E., 2002. Experiencing residential segregation -a contemporary study of Washington, DC. Urban Aff. rev. 38, 155-183.
68. Su, S.L., Xiao, R., Zhang, Y., 2012. Multi-scale analysis of spatially varying rela-tionships between agricultural landscape patterns and urbanization usinggeographically weighted regression. Appl. Geogr. 32, 360-375.
69. Sutton, P.C., 2003. A scale-adjusted measure of "Urban sprawl" using nighttimesatellite imagery. Remote Sens. Environ. 86, 353-369.
70. Tu, J., 2013. Spatial variations in the relationships between land use and water qualityacross an urbanization gradient in the watersheds of Northern Georgia, USA. Environ. Manag. 51, 1-17.
71. Tu, J., Xia, Z.G., 2008. Examining spatially varying relationships between land useand water quality using geographically weighted regression. I: model designand evaluation. Sci. Total Environ. 407, 358-378.
72. United Nations, Department of Economic and Social Affairs, Population Division,2014. World Urbanization Prospects: The 2014 Revision.
73. Verburg, P.H., Eickhout, B., van Meijl, H., 2008. A multi-scale, multi-model approachfor analyzing the future dynamics of European land use. Ann. Reg. Sci. 42, 57-77.
74. Xian, G., Crane, M., 2005. Assessments of urban growth in the Tampa Bay watershedusing remote sensing data. Remote Sens. Environ. 97, 203-215.
75. Xian, G., Crane, M., 2006. An analysis of urban thermal characteristics and associatedland cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sens. Environ. 104, 147-156.
76. Xiao, J.Y., Shen, Y.J., Ge, J.F., Tateishi, R., Tang, C.Y., Liang, Y.Q., Huang, Z.Y., 2006. Evaluating urban expansion and land use change in Shijiazhuang, China, by usingGIS and remote sensing. Landsc. Urban Plan. 75, 69-80.
77. Yang, L.M., Xian, G., Klaver, J.M., Deal, B., 2003. Urban land cover change detec-tion through sub-pixel imperviousness mapping using remotely sensed data. Photogramm. Eng. Remote Sens. 69, 1003-1010.
78. Yuan, F., Sawaya, K.E., Loeffelholz, B.C., Bauer, M.E., 2005. Land cover classifica-tion and change analysis of the twin cities (Minnesota) Metropolitan areaby multitemporal Landsat remote sensing. Remote Sens. Environ. 98, 317-328.
79. Zhang, L.J., Gove, J.H., 2005. Spatial assessment of model errors from four regressiontechniques. For. Sci. 51, 334-346.