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2016 | 35 | 1 |

Tytuł artykułu

Using GEOBIA and data fusion approach for land use and land cover mapping

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline the expensive mapping process. The aim of the study was to create LULC maps of three selected water catchment areas in South Poland using a Geographic Object-Based Image Analysis (GEOBIA) in order to highlight the advantages of this innovative, semi-automatic method of image analysis. The classification workflow included: multi-stage and multi-scale analyses based on a data fusion approach. Input data consisted mainly of BlackBridge (RapidEye) high resolution satellite imagery, although for distinguishing particular LULC classes, additional satellite images (LANDSAT TM5) and GIS-vector data were used. Accuracy assessment of GEOBIA classification results varied from 0.83 to 0.87 (Kappa), depending on the specific catchment area. The main recognized advantages of GEOBIA in the case study were: performing of multi-stage and multi-scale image classification using different features for specific LULC classes and the ability to using knowledge-based classification in conjunction with the data fusion approach in an efficient and reliable manner.

Wydawca

-

Rocznik

Tom

35

Numer

1

Opis fizyczny

p.93-104,fig.,ref.

Twórcy

autor
  • Institute of Forest Resources Management, University of Agriculture in Krakow, Krakow, Poland
autor
  • Institute of Forest Resources Management, University of Agriculture in Krakow, Krakow, Poland
autor
  • Institute of Forest Resources Management, University of Agriculture in Krakow, Krakow, Poland
  • ProGea Consulting, Faculty of Forestry, Kraków, Poland
autor
  • ProGea Consulting, Faculty of Forestry, Kraków, Poland

Bibliografia

  • Arvor D., Durieux L., Andrés S., Laporte M.A., 2013. Advances in Geographic Object-Based Image Analysis with Ontologies: A Review of Main Contributions and Limitations from a Remote Sensing Perspective. ISPRS Journal of Photogrammetry and Remote Sensing 82 (August): 125–137. DOI: 10.1016/j.isprsjprs.2013.05.003.
  • Baatz M., Hoffmann C., Willhauck G., 2008. Progressing from Object-Based to Object-Oriented Image Analysis. In: Blaschke T., Lang S., Hay G.J. (eds), Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, 1st ed., 29–42. Springer, Berlin–Heidelberg.
  • Baatz M., Schape A., 2000. Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. Journal of Photogrammetry and Remote Sensing 58(3–4): 12–23.
  • Benz U., Hofmann P., Willhauck G., Lingenfelder I., Heynen M., 2004. Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information. ISPRS Journal of Photogrammetry and Remote Sensing 58(3–4): 239–258. DOI: 10.1016/j.isprsjprs.2003.10.002.
  • Blaschke T., Hay G.J., Kelly M., Lang S., Hofmann P., Addink E., Queiroz Feitosa R., et al., 2014. Geographic Object-Based Image Analysis – Towards a New Paradigm. ISPRS Journal of Photogrammetry and Remote Sensing 87 (January): 180–191. DOI: 10.1016/j.isprsjprs.2013.09.014.
  • Blaschke T., 2010. Object Based Image Analysis for Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1): 2–16. DOI: 10.1016/j.isprsjprs.2009.06.004.
  • Burnett C., Blaschke T., 2003. A Multi-Scale Segmentation/object Relationship Modelling Methodology for Landscape Analysis. Ecological Modelling 168(3): 233–49. DOI: 10.1016/S0304-3800(03)00139-X.
  • Fink M., Krause P., Kralisch S., Bende-Michl U., Flügel WA., 2007. Development and Application of the Modelling System J2000-S for the EU-Water Framework Directive. Advances in Geosciences 11: 123–30.
  • Flügel A.W., 2009. Applied Geoinformatics for Sustainable IWRM and Climate Change Impact Analysis. Technology, Resource Management & Development 6: 57–85.
  • Hay G.J., Castilla G., 2006. Object-Based Image Analysis: Strengths, Weaknesses, Opportunities and Threats (SWOT). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 36: 4.
  • Hofmann P., Blaschke T., Strobl J., 2011. Quantifying the Robustness of Fuzzy Rule Sets in Object-Based Image Analysis. International Journal of Remote Sensing 32(22): 37–41.
  • Homer C., Huang C., Yang L., Wylie B., Coan M., 2004. Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering & Remote Sensing 70(7): 829–840. DOI: 10.14358/PERS.70.7.829.
  • Lu D., Weng Q., 2007. A Survey of Image Classification Methods and Techniques for Improving Classification Performance. International Journal of Remote Sensing 28(5): 823–870. DOI: 10.1080/01431160600746456.
  • Marinho E., Fasbender D., de Kok R., 2012. Spatial Assessment of Categorical Maps: A Proposed Framework. In: Proceedings of the 4th GEOBIA, 602–607. Rio de Janeiro, Brazil: São José dos Campos: INPE.
  • Molenaar M., 2001. Hierarchical Object-Based Image Analysis of High-Resolution Imagery for Urban Land Use Classification. In: IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Area, 35–39. Rome, University of Rome LA Sapienza: IEEE. DOI: 10.1109/DFUA.2001.985721.
  • Nussbaum S., Niemeyer I., Canty M.J., 2006. SEaTH – A New Tool for Automated Feature Extraction in the Context of Object-Oriented Image Analysis. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-4/C4.
  • Saadat H., Adamowski J., Bonnell R., Sharifi F., Namdar M., Ale-Ebrahim S., 2011. Land Use and Land Cover Classification over a Large Area in Iran Based on Single Date Analysis of Satellite Imagery. ISPRS Journal of Photogrammetry and Remote Sensing 66(5): 608–619. DOI: 10.1016/j.isprsjprs.2011.04.001.
  • Salehi B., Zhang Y., Zhong M., 2013. A Combined Object- and Pixel-Based Image Analysis Framework for Urban Land Cover Classification of VHR Imagery. Photogrammetric Engineering & Remote Sensing 79(11): 999–1014. DOI: 10.14358/PERS.79.11.999.
  • Schöpfer E., Lang S., 2006. Object Fate Analysis – a Virtual Overlay Method for the Categorisation of Object Transition and Object-Based Accuracy Assessment In: 1st International Conference on Object-Based Image Analysis (OBIA 2006). Salzburg.
  • Smith G.M., Morton R.D., 2010. Real World Objects in GEOBIA through the Exploitation of Existing Digital Cartography and Image Segmentation. Photogrammetric Engineering & Remote Sensing 76(2): 163–171. DOI: 10.14358/PERS.76.2.163.
  • Tiede D., Lang S., Albrecht F., Hölbling D., 2010. Object-Based Class Modeling for Cadastre-Constrained Delineation of Geo-Objects. Photogrammetric Engineering & Remote Sensing 76(2): 193–202. DOI: 10.14358/PERS.76.2.193.
  • Varga OG., Szabó S., Túri Z., 2014. Efficiency Assessments of GEOBIA in Land Cover Analysis, NE Hungary. Bulletin of Environmental and Scientific Research.
  • Willhauck G., Schneider T., de Kok R., Ammer U., 2000. Comparison of Object Oriented Classification Techniques and Standard Image Analysis for the Use of Change Detection between SPOT Multispectral Satellite Images and Aerial Photos. ISPRS Archives XXXIII (Supplement B3): 214–221.
  • Xie F., Chen D., Meligrana J., 2013. Selecting Key Features for Remote Sensing Classification by Using Decision-Theoretic Rough Set Model. Photogrammetric Engineering & Remote Sensing 79(9): 787–797.

Typ dokumentu

Bibliografia

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Identyfikator YADDA

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