A Conceptual Framework for Uncertainty Investigation in Map‐based Land Cover Change Modelling

Uncertainty research represents a research stream of high interest within the community of geographical information science. Its elements, terminology and typology are still under strong discussion and adopted methods for analysis are currently under intensive development. This paper presents a conceptual framework for systematic investigation of uncertainty which occurs in applications of land cover change modelling in Geographical Information Systems (GIS) based on historical map data. Historical, in this context, means the map is old enough to allow identification of changes in landscape elements of interest, such as vegetation. To date such analyses are rarely conducted or not satisfactorily carried out, despite the fact that historical map data represent a potentially rich information source. The general validity and practicability of the framework for related applications is demonstrated with reference to one example in which forest cover change in Switzerland is investigated. The conceptual model consists of three domains in which main potential sources of uncertainty are systematically exposed. Existing links between data quality research and uncertainty are investigated to access the complex nature of uncertainty and to characterise the most suitable concepts for analysis. In accordance with these concepts appropriate methods and procedures are suggested to assess uncertainty in each domain. One domain is the production-oriented amount of uncertainty which is inherent in the historical map. Vagueness and ambiguity represent suitable concepts for analysis. Transformation-oriented uncertainty as the second domain occurs owing to editing and processing of digital data. Thereby, the suitable concept of uncertainty is error. The third domain is the application-oriented uncertainty which occurs in comparing semantically different data. This domain relates to multi-temporal discord which assumes the assessment of ‘equi-temporal’ ambiguity and is thus connected to the production-oriented domain. The framework provides an estimation of the overall amount of uncertainty. This can be linked to subsequent assessment of ‘fitness for use’. Thus the model provides a practicable and systematic approach to access the complex nature of uncertainty in the scope of land cover change modelling.

[1]  Peter Fisher,et al.  Sorites paradox and vague geographies , 2000, Fuzzy Sets Syst..

[2]  Achille C. Varzi.  Vagueness in geography , 2001 .

[3]  M. Goodchild,et al.  Geographic Information Systems and Science (second edition) , 2001 .

[4]  Andrej Vckovski,et al.  CHAPTER FIVE – Completeness , 1995 .

[5]  Wenzhong Shi.  A Generic Statistical Approach for Modelling Error of Geometric Features in GIS , 1998, Int. J. Geogr. Inf. Sci..

[6]  Stefano Tarantola,et al.  Uncertainty and sensitivity analysis: tools for GIS-based model implementation , 2001, Int. J. Geogr. Inf. Sci..

[7]  Lars Kulik,et al.  A Geometric Theory of Vague Boundaries Based on Supervaluation , 2001, COSIT.

[8]  B. Bennett.  What is a Forest? On the Vagueness of Certain Geographic Concepts , 2001 .

[9]  Alex Hagen,et al.  Fuzzy set approach to assessing similarity of categorical maps , 2003, Int. J. Geogr. Inf. Sci..

[10]  Stephen C. Guptill.  CHAPTER EIGHT – Temporal information , 1995 .

[11]  Donna Peuquet,et al.  A conceptual framework for incorporating cognitive principles into geographical database representation , 2000, Int. J. Geogr. Inf. Sci..

[12]  Giles M. Foody,et al.  Uncertainty in Remote Sensing and GIS: Fundamentals , 2006 .

[13]  R. Pontius.  QUANTIFICATION ERROR VERSUS LOCATION ERROR IN COMPARISON OF CATEGORICAL MAPS , 2000 .

[14]  Niklaus E. Zimmermann,et al.  A Predictive Uncertainty Model for Field-Based Survey Maps Using Generalized Linear Models , 2004, GIScience.

[15]  M. Molenaar.  The extensional uncertainty of spatial objects. , 1997 .

[16]  Michael F. Goodchild,et al.  Development and test of an error model for categorical data , 1992, Int. J. Geogr. Inf. Sci..

[17]  Jane Drummond,et al.  CHAPTER THREE – Positional accuracy , 1995 .

[18]  George J. Klir,et al.  Uncertainty-Based Information , 1999 .

[19]  Helen Couclelis,et al.  The Certainty of Uncertainty: GIS and the Limits of Geographic Knowledge , 2003 .

[20]  Brandon Plewe,et al.  The Nature of Uncertainty in Historical Geographic Information , 2002, Trans. GIS.

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

[22]  M. Goodchild,et al.  Spatial Uncertainty in Ecology , 2001, Springer New York.

[23]  Peter Fisher,et al.  Alternative Set Theories for Uncertainty in Spatial Information , 2001 .

[24]  Helen M. Regan,et al.  Mapping epistemic uncertainties and vague concepts in predictions of species distribution , 2002 .

[25]  John Daintith,et al.  A Dictionary of Computing , 1986 .

[26]  Stephen V. Stehman,et al.  Basic probability sampling designs for thematic map accuracy assessment , 1999 .

[27]  Howard Veregin,et al.  CHAPTER NINE – An evaluation matrix for geographical data quality , 1995 .

[28]  MICHAEL F. GOODCHILD,et al.  A Simple Positional Accuracy Measure for Linear Features , 1997, Int. J. Geogr. Inf. Sci..

[29]  Arnold Bregt,et al.  Assessing fitness for use: the expected value of spatial data sets , 2001, Int. J. Geogr. Inf. Sci..

[30]  Michael F. Goodchild,et al.  Issues of quality and uncertainty , 1991 .

[31]  G. Klir,et al.  Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .

[32]  Henri J G L Aalders.  The Registration of Quality in a GIS , 2002 .

[33]  François Walter,et al.  Attitudes towards the environment in Switzerland, 1880–1914 , 1989 .

[34]  Giles M. Foody,et al.  Uncertainty in Remote Sensing and GIS: Foody/Uncertainty in Remote Sensing and GIS , 2006 .

[35]  Michael F. Goodchild,et al.  CHAPTER FOUR – Attribute accuracy , 1995 .

[36]  Ursula C. Benz,et al.  Measures of classification accuracy based on fuzzy similarity , 2000, IEEE Trans. Geosci. Remote. Sens..

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

[38]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[39]  Vincent B. Robinson,et al.  A Perspective on the Fundamentals of Fuzzy Sets and their Use in Geographic Information Systems , 2003, Trans. GIS.

[40]  R. G. Pontlus.  Quantification Error Versus Location Error in Comparison of Categorical Maps , 2006 .

[41]  F. Wang,et al.  Handling Grammatical Errors, Ambiguity and Impreciseness in GIS Natural Language Queries , 2003, Trans. GIS.

[42]  Andrew U. Frank,et al.  Tiers of ontology and consistency constraints in geographical information systems , 2001, Int. J. Geogr. Inf. Sci..

[43]  Stephen V. Stehman,et al.  Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles , 1998 .

[44]  Michael Tye,et al.  Sorites Paradoxes and the Semantics of Vagueness , 1994 .

[45]  Nicholas Chrisman,et al.  Part 2: Issues and Problems Relating to Cartographic Data Use, Exchange and Transfer: The Role Of Quality Information In The Long-Term Functioning Of A Geographic Information System , 1984 .

[46]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[47]  K. Lowell,et al.  Spatial Accuracy Assessment : Land Information Uncertainty in Natural Resources , 1999 .

[48]  A. Morris,et al.  A Framework for Modeling Uncertainty in Spatial Databases , 2003, Trans. GIS.

[49]  Ola Ahlqvist,et al.  Rough classification and accuracy assessment , 2000, Int. J. Geogr. Inf. Sci..

[50]  S. Fotheringham,et al.  Geographically weighted summary statistics — aframework for localised exploratory data analysis , 2002 .

[51]  P. Atkinson,et al.  Current status of uncertainty issues in remote sensing and GIS , 2002 .

[52]  Wenzhong Shi,et al.  A stochastic process-based model for the positional error of line segments in GIS , 2000, Int. J. Geogr. Inf. Sci..

[53]  Hugh G. Lewis,et al.  A generalized confusion matrix for assessing area estimates from remotely sensed data , 2001 .

[54]  Peter A. Burrough,et al.  Natural Objects with Indeterminate Boundaries , 2020 .

[55]  Ola Ahlqvist,et al.  Rough and fuzzy geographical data integration , 2003, Int. J. Geogr. Inf. Sci..

[56]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[57]  Models of uncertainty in spatial data , 2022 .