000000280 001__ 280
000000280 020__ $$a   90-6164-220-5
000000280 041__ $$aEnglish
000000280 100__ $$aXinming Tang
000000280 245__ $$aSpatial object modelling in fuzzy topological spaces with application to land cover change
000000280 260__ $$c2004
000000280 260__ $$bInternational Inst. for Aerospace Survey and Earth Sciences - ITC, Enschede
000000280 300__ $$a219
000000280 440__ $$a   ITC dissertation
000000280 440__ $$n  108
000000280 502__ $$aThesis (Ph. D.) - University of Twente, Wageningen, The Netherlands
000000280 520__ $$aCurrently most GISs represent natural phenomena by crisp spatial objects. In fact many  natural phenomena have fuzzy characteristics. The representation of these objects in the  crisp form greatly simplifies the processing of spatial data. However, this simplification  cannot describe these natural phenomena precisely, and it will lead to loss of  information in these objects. In order to describe natural phenomena more precisely, the  fuzziness in these natural phenomena should be considered and represented in a GIS.  This will allow the derivation of better results and a better understanding of the real  world to be achieved.   The central topic of this thesis focuses on the accommodation of fuzzy spatial objects in  a GIS. Several issues are discussed theoretically and practically, including the definition  of fuzzy spatial objects, the topological relations between them, the modeling of fuzzy  spatial objects, the generation of fuzzy spatial objects and the utilization of fuzzy spatial  objects for land cover changes.   A formal definition of crisp spatial objects has been derived based on the highly abstract  mathematics such as set theory and topology. Fuzzy set theory and fuzzy topology are  the ideal tools for defining fuzzy spatial objects theoretically, since fuzzy set theory is a  natural extension of classical set theory and fuzzy topology is built based on fuzzy sets.  However, owing to the extension, several properties holding between crisp sets do not  hold for fuzzy sets.   The key issue of a fuzzy spatial object is its boundary. Three definitions of fuzzy  boundary are revisited and one is selected  for the definition of fuzzy spatial objects.  Besides the fuzzy boundary, several notions such as the core, the internal, the fringe, the  frontier, the internal fringe and the outer of a fuzzy set are defined in fuzzy topological  space. The relationships between these notions and the interior, the boundary and the  exterior of a fuzzy set are revealed. In general, the core is the crisp subset of the interior,  and the fringe is a kind of boundary but shows a finer structure than the boundary of a  fuzzy set in fuzzy topological space. These notions are all proven to be topological  properties of a fuzzy topological space.   The definition of a simple fuzzy region is derived based on the above topological  properties. It is discussed twice in the thesis. Firstly, the definition of a simple fuzzy  region is given in a special fuzzy topological space called crisp fuzzy topological space,  since most topological properties of a fuzzy set in the fuzzy topological space are the  same as those in crisp topological space. A formal definition of a simple fuzzy region is  proposed based on the discussion of the topological properties, besides the interior, the  boundary and the exterior, of a fuzzy set in the general fuzzy topological space. A crisp  simple region is a special form of a simple fuzzy region.   One of the fundamental properties between fuzzy spatial objects is the topological  relations. This topic is intensively discussed in the thesis. The problem of the 9-intersection approach for identifying topological relations between fuzzy spatial  objects is revealed. In order to derive the topological relations between fuzzy spatial  objects, the 9-intersection approach is updated into the 3*3-intersection approach in the  crisp fuzzy topological space. Furthermore,  the 4*4-intersection matrix is built up by  using the topological properties of fuzzy sets, and the 5*5-intersection matrix can be  built up based on a certain condition in crisp fuzzy topological space. These matrices  are then updated in the general fuzzy topological space, based on topological properties,  other than the interior, the boundary and the exterior, of two fuzzy sets. Two  3*3-intersection and one 4*4-intersection matrices are introduced in the general fuzzy  topological space. The topological relations between simple fuzzy regions can be  identified based on the topological invariants in the intersections of the matrices. Using  the empty/non-empty topological invariants in the intersections, 44 and 152 relations  are derived between two simple fuzzy regions.   The modeling of fuzzy spatial objects should be done not only for simple fuzzy regions,  but also for fuzzy lines and fuzzy points. In order to model fuzzy lines and fuzzy points  and the topological relations between fuzzy spatial objects, a fuzzy cell is proposed and  a fuzzy cell complex can be constructed from fuzzy cells. A fuzzy region, a fuzzy line  and a fuzzy point are then defined according to this structure. The relations between  these fuzzy spatial objects are identified. The fuzzy cell complex structure constitutes a  theoretic framework, since it can easily model the fuzzy spatial objects.   After proposing the theoretic framework for fuzzy spatial object modeling, the thesis  addresses several practical issues on applying fuzzy spatial objects. The first issue is  how to generate fuzzy spatial objects. A composite method is proposed for the  generation of fuzzy land cover objects. It involves several steps, from designing  membership functions to classification and  refining the membership values of fuzzy  land cover objects.   Another practical issue is how to retrieve fuzzy spatial objects, particularly on the basis  of topological relations. In traditional GIS, the query operators are defined based on the  relatively small number of topological relations. However, there are many topological  relations between fuzzy spatial objects. In order to query fuzzy spatial objects, the query  operators are proposed and formalized  based on the common-sense operators in  traditional GIS. The 44 or 152 topological relations are grouped into these operators by  four different methods. These methods constitute a relatively complete covering for  querying fuzzy spatial objects so as to meet the different application requirements.  The third practical issue is how to use fuzzy spatial objects in real applications. Since  the dynamics of land covers is a very important topic in China, the focus lies on  calculating changes of land covers. Sanya city, located in south China, is selected as the  test area. A fuzzy reasoning method is proposed for calculating land cover changes. It  shows that, with fuzzy representation, not only can a better result be achieved for the  land cover changes, but also the details of changes can be revealed.  
000000280 653__ $$aNatural hazards
000000280 653__ $$aGeographical information systems
000000280 653__ $$aTopography
000000280 653__ $$aLand information systems
000000280 653__ $$aRemote sensing
000000280 8564_ $$uhttp://doc.utwente.nl/41448/1/thesis_tang.pdf$$yDownload
000000280 980__ $$aTHESIS