Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography

International Journal of Applied Earth Observation and Geoinformation

Volumn 33, Issue 1, 2014, Pages 321-330

  Li, Xiaoxiao ^a   Myint, Soe W ^a   Zhang, Yujia ^a   Galletti, Chritopher ^a   Zhang, Xiaoxiang ^b   Turner II, Billie L ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

^b HOHAI UNIVERSITY   (China)

Author keywords

Aerial photography; Classification system; Land cover; Object based image analysis; Phoenix; Urban

Indexed keywords

AERIAL PHOTOGRAPHY; GIS; IMAGE ANALYSIS; LAND COVER; METROPOLITAN AREA; SPATIAL RESOLUTION; SUSTAINABILITY;

ARIZONA; PHOENIX; UNITED STATES;

EID: 84901825064     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.04.018     Document Type: Article

References (32)

1. Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E., 1976. A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper 964, 28.
2. Baatz, M., Schape, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. J. Photogram. Remote Sens. 58,12-23.
3. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heyen, M., 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogram. Remote Sens. 58 (3), 239-258.
4. Bhaskaran, S., Paramananda, S., Ramnarayan, M., 2010. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data.Appl. Geogr. 30 (4), 650-665.
5. Blaschke, T., 2010. Object-based image analysis for remote sensing. ISPRS J. Photogram. Rem. Sens. 65 (1), 2-16.
6. Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.L., 2001. Color image segmentation: advancesand prospects. Pattern Recogn. 34 (12), 2259-2281.
7. Cleve, C., Kelly, M., Kearns, F.R., Moritz, M., 2008. Classification of the wildland-urbaninterface: a comparison of pixel- and object-based classifications using high-resolution aerial photography. Comput. Environ. Urban Syst. 32 (4), 317-326.
8. Connors, J.P., Galletti, C.S., Chow, W.T., 2013. Landscape configuration and urban heatisland effects: assessing the relationship between landscape characteristics andland surface temperature in Phoenix, Arizona. Landsc. Ecol. 28 (2), 271-283.
9. Definiens, 2009. eCognition Developer 8. Definiens AG, München, Germany.
10. ERDAS, 2014. Leica Geosystems Geospatial Imaging, LLC (2005) ERDAS Field Guide,Norcross, GA, USA.
11. Gong, P., Marceau, D.J., Howarth, P.J., 1992. A comparison of spatial feature-extraction algorithms for land-use classification with SPOT HRV data. Remote Sens. Environ. 40 (2), 137-151.
12. Li, X., Shao, G., 2013. Object-based urban vegetation mapping with high-resolution aerial photography as a single data source. Int. J. Remote Sens. 34 (3), 771-789.
13. Middel, A., Brazel, J.A., Hagen, B., Myint, W.S., 2011. Land cover modification scenarios and their effects on daytime heating in the inner core residential neighborhoods of Phoenix, AZ, USA. J. Urban Technol. 18 (4),61-79.
14. Morgan, J.L., Gergel, S.E., 2010. Quantifying landscape heterogeneity from historicaerial photographs using object-based analysis. Landsc. Ecol. 25, 985-998.
15. Myint, S.W., Galletti, C.S., Kaplan, S., Kim, K.W., 2013a. Object vs. pixel: a systematic evaluation in urban environments. Geocarto Int. 28 (7), 657-678.
16. Myint, S.W., Wentz, E.A., Brazel, A.J., Quattrochi, D.A., 2013b. The impact of distinct anthropogenic and vegetation features on urban warming. Landsc. Ecol. 28 (5),959-978.
17. Myint, S.W., 2012. The effects of the spatial pattern of vegetation cover on urbanwarming in a desert city. In: Yang, X., Li, J. (Eds.), Advances in Mapping from Aerospace Imagery: Techniques and Applications. CRC Press, Boca Raton, FL, pp.261-278.
18. Myint, S.W., Gober, P., Brazel, A., Grossman-Clark, S., Weng, Q., 2011. Per-pixel vs.object-based classification of urban land cover extraction using high spatialresolution imagery. Remote Sens. Environ. 115 (5), 1145-1161.
19. NRC (National Research Council), 2013. Advancing Land Change Modeling: Oppor-tunities and Requirements. National Academies Press, Washington, DC.
20. Seto, K., Reenberg, A. (Eds.), 2014. Rethinking Global Land Use in an Urban Era. Ernest Strügmann Forum. MIT Press, Cambridge.
21. Shackelford, A.K., Davis, C.H., 2003. A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sens. 41 (9), 1920-1932.
22. Stefanov, W.L., Ramsey, M.S., Christensen, P.R., 2001. Monitoring urban land coverchange: an expert system approach to land cover classification of semiarid toarid urban centers. Remote Sens. Environ. 77 (2), 173-185.
23. Stefanov, L.W., Netzband, M., 2005. Assessment of ASTER land cover and MODISNDVI data at multiple scales for ecological characterization of an arid urbancenter. Remote Sens. Environ. 99 (1-2), 31-43.
24. Turner II, B.L., Janetos, A.C., Verburg, P.H., Murray, A.T., 2013. Land system architecture: using land systems to adapt and mitigate global environmental change.Global Environ. Chang. 23 (2), 395-397.
25. Walker, J.S., Briggs, J.M., 2007. An object-oriented approach to urban forest mappingin Phoenix. Photogram. Eng. Remote Sens. 73 (5), 577-583.
26. Wentz, A.E., Stefanov, W.L., Gries, C., Hope, D., 2006. Land use and land cover mapping from diverse data sources for an arid urban environments. Comput. Environ.Urban Syst. 30 (3), 320-346.
27. Wu, J.G., 2013. Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landsc. Ecol. 28, 999-1023.
28. Yu, Q., Gong, P., Chinton, N., Biging, G., Kelly, M., Schirokauer, D., 2006. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogram. Eng. Remote Sens. 72 (7), 799-811.
29. Zhou, W., Troy, A., 2008. An object-oriented approach for analysing and characterizing urban landscape at the parcel level. Int. J. Remote Sens. 29 (11), 3119-3135.
30. Zhou, W., Troy, A., Grove, J.M., 2008. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multi-temporal highre solution remote sensing data. Sensors 2008 (8), 1613-1636.
31. Zhou, W., Huang, G., Troy, A., Cadenasso, M.L., 2009. Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: acomparison study. Remote Sens. Environ. 113, 1769-1777.
32. Zhou, W., 2013. An object-based approach for urban land cover classification: integrating LiDAR height and intensity data. IEEE Geosci. Remote Sens. Lett. 10 (4),928-931.