Extending post-classification change detection using semantic similarity metrics to overcome class heterogeneity: A study of 1992 and 2001 U.S. National Land Cover Database changes

The use of post-classification change methods for the analysis of land cover change provides intuitive and potentially reliable results. A recurring problem is the difference in land cover nomenclature that can occur over time or across space when multiple data sources are required. Building on work that uses category semantics as a foundation for reasoning with land cover classes, this paper uses a fuzzy sets based approach to develop attribute based prototype definitions of land cover classes. These formalized category descriptions are used to look at land cover changes as a semantic change evaluated through semantic similarity metrics. The methodology is illustrated on the U.S. National Land Cover Data from 1992 to 2001 over Chester County, PA. The results demonstrate that the proposed method is more versatile than the standard post-classification method in that it can both provide an overall, spatially explicit evaluation of land cover change, as well as nuanced assessments of graded changes for heterogeneous land cover types.

[1]  Peter Gärdenfors,et al.  Conceptual spaces - the geometry of thought , 2000 .

[2]  Sucharita Gopal,et al.  Fuzzy set theory and thematic maps: accuracy assessment and area estimation , 2000, Int. J. Geogr. Inf. Sci..

[3]  Madan M. Gupta,et al.  Introduction to Fuzzy Arithmetic , 1991 .

[4]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[5]  Toby N. Carlson,et al.  Land Use Dynamics of Chester County, Pennsylvania, from a Satellite Remote Sensing Perspective , 2000 .

[6]  Chen-Chieh Feng,et al.  Assessment of semantic similarity between land use/land cover classification systems , 2004, Comput. Environ. Urban Syst..

[7]  Yaser A. Bishr,et al.  Overcoming the Semantic and Other Barriers to GIS Interoperability , 1998, Int. J. Geogr. Inf. Sci..

[8]  Giles M. Foody,et al.  Detection of partial land cover change associated with the migration of inter-class transitional zones , 1999 .

[9]  Steffen Fritz,et al.  Comparison of land cover maps using fuzzy agreement , 2005, Int. J. Geogr. Inf. Sci..

[10]  A. D. Gregorio,et al.  Land Cover Classification System (LCCS): Classification Concepts and User Manual , 2000 .

[11]  D. Lu,et al.  Change detection techniques , 2004 .

[12]  Peter F. Fisher,et al.  The evaluation of fuzzy membership of land cover classes in the suburban zone , 1990 .

[13]  G. Foody,et al.  Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions , 1994 .

[14]  Sara Irina Fabrikant,et al.  Spatialization Methods: A Cartographic Research Agenda for Non-geographic Information Visualization , 2003 .

[15]  Peter F. Fisher,et al.  Integrating land-cover data with different ontologies: identifying change from inconsistency , 2004, Int. J. Geogr. Inf. Sci..

[16]  S. Guptill,et al.  Elements of Spatial Data Quality , 1995 .

[17]  Alan H. Strahler,et al.  Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change pro , 1994 .

[18]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[19]  Martin Jung,et al.  Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .

[20]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[21]  W. A. Labuschagne.  Review: Peter Gärdenfors, Conceptual Spaces: "The Geometry of Thought" (Cambridge Mass.: MIT, 2000) , 2005 .

[22]  A. Comber,et al.  Assessment of a Semantic Statistical Approach to Detecting Land Cover Change Using Inconsistent Data Sets , 2004 .

[23]  Marinos Kavouras,et al.  A method for the formalization and integration of geographical categorizations , 2002, Int. J. Geogr. Inf. Sci..

[24]  M. Gahegan,et al.  Probing the Relationship Between Classification Error and Class Similarity , 2005 .

[25]  François Salgé.  CHAPTER SEVEN – Semantic accuracy , 1995 .

[26]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[27]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[28]  Peter F. Fisher,et al.  Fuzzy sets , 1965 .

[29]  Karen Jensen,et al.  Disambiguating Prepositional Phrase Attachments by Using On-Line Dictionary Definitions , 1987, Comput. Linguistics.

[30]  Frederico T. Fonseca,et al.  Using Ontologies for Integrated Geographic Information Systems , 2002, Trans. GIS.

[31]  Robert Gilmore Pontius,et al.  A generalized cross‐tabulation matrix to compare soft‐classified maps at multiple resolutions , 2006, Int. J. Geogr. Inf. Sci..

[32]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[33]  Ola Ahlqvist,et al.  A Parameterized Representation of Uncertain Conceptual Spaces , 2004, Trans. GIS.

[34]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[35]  Mark Gahegan,et al.  A Situated Knowledge Representation of Geographical Information , 2006, Trans. GIS.

[36]  M. Bauer,et al.  Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing , 2005 .

[37]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[38]  J. Mas.  Monitoring land-cover changes: A comparison of change detection techniques , 1999 .

[39]  Ola Ahlqvist,et al.  Please Scroll down for Article International Journal of Geographical Information Science Using Uncertain Conceptual Spaces to Translate between Land Cover Categories Using Uncertain Conceptual Spaces to Translate between Land Cover Categories , 2022 .

[40]  C. Woodcock,et al.  Theory and methods for accuracy assessment of thematic maps using fuzzy sets , 1994 .

[41]  Nicola Guarino,et al.  Formal ontology, conceptual analysis and knowledge representation , 1995, Int. J. Hum. Comput. Stud..

[42]  John F. Sowa,et al.  Knowledge Representation Logical, Philosophical, and Computational Foundations A Review , 2000 .

[43]  S. Stehman,et al.  Accuracy Assessment , 2003 .

[44]  Caroline M. Eastman,et al.  Response: Introduction to fuzzy arithmetic: Theory and applications : Arnold Kaufmann and Madan M. Gupta, Van Nostrand Reinhold, New York, 1985 , 1987, Int. J. Approx. Reason..

[45]  Stephen V. Stehman,et al.  A Strategy for Estimating the Rates of Recent United States Land-Cover Changes , 2002 .

[46]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .