Skip to main content
Log in

Urban landscape extraction and analysis based on optical and microwave ALOS satellite data

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Accurate mapping of urban land cover from satellite data provides essential input to urban landscape analysis, modelling and urban ecosystem studies. Additionally, analysis of urban landscape metrics will provide a positive step towards comprehensive understanding of the features of urban landscape structure and further planning. In the present study, multi-spectral Advanced Land Observing Satellite (ALOS)/Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) images and ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) dual-polarized (FBD) microwave images were used to extract urban land cover information by applying the decision tree method, and additional Advanced Space borne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER/GDEM) was used to reduce the effects of mountains in Synthetic Aperture Radar (SAR) images due to high backscattering from urban construction land. A set of landscape metrics, such as landscape diversity, edge density and landscape shape indices with supplementary ecological meanings, were chosen to quantitatively analysis urban landscape patterns in arid environments. The overall accuracy assessment result was 91.50%, and the experimental results demonstrate that synergetic use of optical and SAR ALOS data has the potential and advantages for Arid Urban Region mapping, while the decision tree method showed intuitive simplicity and computational efficiency. The quantitative analysis results of landscape metrics showed that distribution of landscape types in Urumqi city were inhomogeneous, the urban landscape dominated by a few classes. Urbanization in this region has resulted in dramatic increases in patch density (PD), edge density (ED) and landscape shape complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Alimujiang K, Tang B, Gulikezi T (2013) Analysis of the spatial-temporal dynamic changes of urban expansion in oasis of Xinjiang based on RS and GIS. J Glaciol Geocryol 35(4):1056–1064

    Google Scholar 

  • Basly L, Cauneau F, Ranchin T, Wald L, 1999 “Sar imagery in urban area,” inProc. 19th Symp. EARSeL—Remote Sens. 21st Century, J.-L. Casanova, Ed. Valladolid, Spain, pp. 563–568.

  • Bauer ME, Loeffelholz BC, Wilson B (2007) Estimating and mapping impervious surface area by regression analysis of Landsat imagery. In remote sensing of impervious surfaces, Q. Weng (Ed.), pp. 3–20. CRC Press Taylor & Francis, London

    Google Scholar 

  • Botequilha-Leitão A, Miller J, Ahern J, et al. (2006) Measuring landscapes. Island Press, Washington, DC, A Planner’s Handbook

    Google Scholar 

  • Breuste J, Feldmann H, Uhlmann O (1998) Urban ecology. Springer, Berlin

    Book  Google Scholar 

  • Buyantuyev A, Wu J, Gries C (2010) Multiscale analysis of the urbanization pattern of the phoenix metropolitan landscape of USA: time, space and thematic resolution. Landsc Urban Plan 94(3–4):206–217

    Article  Google Scholar 

  • Chen LD, LiuY LYH, et al. (2008) Acta. Ecologica Sinica 28(11):5521–5531

    Article  Google Scholar 

  • Chen J et al. (2015) Global land cover mapping at 30 m resolution: a POK-based operational approach. ISPRS J Photogramm Remote Sens 103:7–27

    Article  Google Scholar 

  • Cushman SA, McGarigal K, Neel MC (2008) Parsimony in landscape metrics: strength, universality and consistency. Ecol Indic 8:691–703

    Article  Google Scholar 

  • DiBari J (2007) Evaluation of five landscape-level metrics for measuring the effects of urbanization on landscape structure: the case of Tucson, Arizona. USA Landsc Urban Plann 79:308–313

    Article  Google Scholar 

  • Elnaggar AA, Noller JS (2010) Application of remote sensing data and decision tree analysis for mapping salt affected soils over large areas. Remote Sens 2:151–165

    Article  Google Scholar 

  • Ewing R, Kostyack J, Chen D, et al. (2005) Endangered by sprawl: how runaway development threatens America’s wildlife. In: National Wildlife Federation, smart growth America, and nature serve. U.S.A, Washington, D.C.

    Google Scholar 

  • Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, New York

    Google Scholar 

  • Forman RTT, Godron M (1986) Landscape ecology. John Wiley, New York

    Google Scholar 

  • Frank A. 1999. Tracing socioeconomic pattern of urban development: issues, problems and methods of spatio-temporal urban analysis. In: Li, B. (Ed.), Geoinformatic and Socioinformatics. The proceedings of Geoinformatics’99 Conference. Ann Arbor, MI, pp. 1–12.

  • Gamba P, Lisini G (2013) Fast and efficient urban extent extraction using ASAR wide swath mode data. IEEE J Select Top Appl Earth Observ Remote Sens 6(5):2184–2195

    Article  Google Scholar 

  • Gustafson EJ (1998) Quantifying landscape spatial pattern: what is the state of the art? Ecosystems 1:143–156

    Article  Google Scholar 

  • Henderson FM, Xia ZG (1997) SAR applications in human settlement detection, population estimation and urban land use pattern analysis: A status report. IEEE Trans Geosci Remote Sens 35:79–85

    Article  Google Scholar 

  • Herold M, Goldstein NC, Clarke KC (2003) The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302

    Article  Google Scholar 

  • Herold M, Couclelis H, Clarke KC (2005) The role of spatial metrics in the analysis and modelling of urban land use change. Comput Environ Urban 29:369–399

    Article  Google Scholar 

  • Hoan N.T, Tateishi R, Bayan A, et al. (2013) Tropical forest mapping using a combination of optical and microwave data of ALOS. Int J Remote Sens 34(1):139–153

  • Huang Y, Chen X, et al. (2006) Urban sprawl pattern and spatial features of Urumqi city during the last 15 years. J Glaciol Geocryol 28(3):364–370

    Google Scholar 

  • Hui Y, Rongqun Z, Xianwen L (2009) Classification of wetland from TM imageries based on decision tree. Inf Sci Appl 6:1155–1164

    Google Scholar 

  • Ikiel, C., Ustaoglu, B., Dutucu, A.A.,& Kilic, D.E. (2013). Remote sensing and GIS-based integrated analysis of land cover change in Duzce Plain and its surroundings(North Western Turky).Environmental monitoring and assessment,185(2),1699–1709

  • Jiang LM, Liao MS, Lin H, et al. (2009) Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong. Int J Remote Sens 30(11):2781–2796

    Article  Google Scholar 

  • Katz B, Liu A (2000) Moving beyond sprawl: toward a broader metropolitan agenda. Brook Rev 18(2):31–34

    Article  Google Scholar 

  • Kim J, Ellis C (2009) Determining the effects of local development regulations on landscape structure: comparison of the woodlands and North Houston. TX Landsc Urban Plann 92:293–303

    Article  Google Scholar 

  • Lee JS (1981) Speckle analysis and smoothing of synthetic aperture radar images. Comp Grap Image Proc 17:24–32

    Article  Google Scholar 

  • Leinenkugel P, Esch T, Kuenzer C (2011) Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data. Remote Sens Environ 115:3007–3019

    Article  Google Scholar 

  • Li H, Wu J (2004) Use and misuse of landscape indices. Landsc Ecol 19:389–399

    Article  Google Scholar 

  • Liquan Z, Jianping W, Yu Z, Jiong S. 2004. A GIS-based gradient analysis of urban landscape pattern of Shanghai metropolitan area, China. Landsc. Urban Plann.69, 1–16.

  • Luck M, Wu J (2002) A gradient analysis of the landscape pattern of urbanization in the phoenix metropolitan area of USA. Landsc Ecol 17:327–339

    Article  Google Scholar 

  • McFeeters SK (1996) The use of normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7):1425–1432

    Article  Google Scholar 

  • McGarigal K, SA Cushman, and E Ene. 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at the following web site: www.umass.edu/landeco/research/fragstats/fragstats.html (last accessed 15 Dec 2014)

  • Mesev V, Gorte B, Longley PA (2001) Modified maximum-likelihood classification algorithms and their application to urban remote sensing. In: Donnay J, Barnsley MJ, Longley PA (eds) Remote sensing and urban analysis. Taylor &Francis Inc, New York, pp. 69–86

    Google Scholar 

  • Mourad B, Kalifa G, Dong CH (2010) Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge. ISPRS J Photogramm Remote Sens 65:143–153

    Article  Google Scholar 

  • Orville R.E, uffines G, Nielsen-Gammon, et al. 2000. Enhancement of cloud-to-ground lightning over Houston, Texas Geogphys Res Lett 28, 2597–25600.

  • Pickett STA, Cadenasso ML, Grove JM, et al. (2001) Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Ann RevEcol Syst 32:127–157

    Article  Google Scholar 

  • Rouse JW Jr, Haas RH, Deering DW, et al. (1974) Monitoring the vernal advancement and Retrogradation (green wave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, MD, 371p

  • Shimada M (2010) Ortho-rectification and slope correction of SAR data using DEM and its accuracy evaluation. IEEE J Sel Top Appl Earth Obs Remote Sens 3(4):657–671

    Article  Google Scholar 

  • Shimada M, Isoguchi O, Tadono T, Isono K (2009) PALSAR radiometric calibration and geo metric calibration. IEEE Trans Geosci Remote Sens 3:765–768

    Google Scholar 

  • Soergel U (2010) Radar remote sensing of urban areas. Springer, Heidelberg, pp. 133–159

    Book  Google Scholar 

  • Tischendorf L (2001) Can landscape indices predict ecological processes consistently. Landsc Ecol 16:235–254

    Article  Google Scholar 

  • Tison C, Nicolas J M, Tupin F et al. 2004. A new statistical model for Markovian classification of urban areas in high-resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing, 42, 2046–2057.

  • Turner MG (2005) Landscape ecology in North America: past, present, and future. Ecology 86(8):1967–1974

    Article  Google Scholar 

  • Uuemaa E, Antrop M, Roosaare J et al. 2009. Landscape metrics and indices: an overview of their use in landscape research. Living Rev Landsc Res 3, http://www.livingreviews.org/lrlr-2009-1 (last accessed 31 Jun 2015)

  • Waske B, van der Linden S (2008) Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Trans Geosci Remote Sens 46:1457–1466

    Article  Google Scholar 

  • Weng QH (2012) Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sens Environ 117:34–49

    Article  Google Scholar 

  • Whitford V, Ennos AR, Handley JF (2001) “City form and natural process”- indicators for the ecological performance of urban areas and their application to Merseyside. UK Landsc Urban Plann 57(2):91–103

    Article  Google Scholar 

  • YANG L, HUANG C, HOMER CG, et al. (2003) An approach for mapping large–area impervious surfaces: synergistic use of Landsat 7 ETM+ and high spatial resolution imagery. Can J Remote Sens 29:230–240

    Article  Google Scholar 

  • Zhang JX, Yang JH, Zhao Z, Li HT, Zhang YH (2010) Block-regression based fusion of optical and SAR imagery for feature enhancement. Int J Remote Sens 31:2325–2345

    Article  Google Scholar 

  • Zhang H, Zhang Y, Lin H (2012) A comparison study of impervious surfaces estimation using optical and SAR remote sensing images. Int J Appl Earth Obs Geoinf 18:148–156

    Article  Google Scholar 

  • Zhang Y, Zhang H, Lin H (2014) Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sens Environ 141:155–167

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the JAXA for providing ALOS images of this study. This research was supported by the Natural Science Foundation of China (41361043), Ministry of Education’s general research project on Humanities and Social Science (11YJCZH001). Finally, we also extend our gratitude to the two anonymous reviewers for their constructive and valuable feedback on earlier versions of this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alimujiang Kasimu.

Additional information

Communicated by: H. A. Babaie

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aimaiti, Y., Kasimu, A. & Jing, G. Urban landscape extraction and analysis based on optical and microwave ALOS satellite data. Earth Sci Inform 9, 425–435 (2016). https://doi.org/10.1007/s12145-016-0264-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-016-0264-4

Keywords

Navigation