The Seventh International Symposium of Spatial Accuracy Assessment in Natural Resources and Environmental Science
July 5-7 2006, Lisbon, Portugal

Accuracy 2006 was the 7th meeting in a series of biannual symposia organised by the International Spatial Accuracy Research Association. It covered topics related with assessment, modelling, visualisation and propagation of uncertainty in spatial data and process models, with an emphasis on themes related with the environment and natural resources.
Keynote speakers
- Yohay Carmel, The Technion, Israel
- Giles Foody, University of Southampton, UK
- Gerard Heuvelink, Wageningen, The Netherlands
- Phaedon Kyriakidis, University of California, USA
- Gil Pontius, Clark University, USA
Preface
Conference report
Plenary Lectures
Carmel Y., Flather C. and Dean D. A methodology for translating positional error into measures of attribute error, and combining the two error sources, pp 3-17
Foody G. M. The evaluation and comparison of thematic maps derived from remote sensing, pp 18-31
Heuvelink G. B. M. Incorporating process knowledge in spatial interpolation of environmental variables, pp 32-47
Kyriakidis P. C. Towards a systems approach to the visualization of spatial uncertainty, pp 48-63
Pontius R. G. Jr and Connors J. Expanding the conceptual, mathematical and practical methods for map comparison, pp 64-80
Oral Communications
Approaches to spatial accuracy assessment
Felicisimo A. M., Cuartero A. and Polo M. E. Analysis of homogeneity and isotropy of spatial uncertainty by means of GPS kinematic check lines and circular statistics, pp 85-90
Griffith D. A. Statistical efficiency of model-informed geographic sampling designs, pp 91-98
Peixoto M., Costa A. C., Painho M. and Bartoschek T. A stratified sampling approach to the spatial accuracy assessment of digital cartography: an applicatio to the Portuguese Ecological Reserve, pp 99-108
Pilz J. and Spöck G. Spatial sampling design for prediction taking account of uncertain covariance structure, pp 109-118
Stevens D.L. Jr. Spatial properties of design-based versus model-based approaches to environmental sampling, pp 119-125
Characterising uncertainty in DEM
Cardenal J., Delgado J., Mata E., González A. and Olague I. Use of historical flight for landslide monitoring, pp 129-138
Fernández T., Delgado J., Cardenal J., Irigaray C., El Hamdouni R. and Chacón J. Improvement of positional accuracy of a landslide database using digital photogrammetry techniques, pp 139-149
Gallant J. C. and Hutchinson M. F. Producing digital elevation models with uncertainty estimates using a multi-scale Kalman filter, pp 150-159
Gonçalves G. Analysis of interpolation errors in urban digital surface models created from LiDAR data, pp 160-168
Gonçalves-Seco L., Miranda D., Crecente R. and Farto J. Digital Terrain Model generation using airborne LiDAR in a forested area of Galicia, Spain, pp 169-180
Sindayihebura A., Van Meirvenne M. and Nsabimana S. Comparison of methods for deriving a Digital Elevation Model from contours and modelling of the associated uncertainty, pp 181-190
de Sousa L., Nery F., Sousa R. and Matos J. Assessing the accuracy of hexagonal versus square tilled grids in preserving DEM surface flow directions, pp 191-200
Error sensitive spatial databases
Belussi A., Brovelli M. A., Negri M., Pelagatti G. and Sansò F. Dealing with multiple accuracy levels in spatial databases with continuous update, pp 203-212
Metadata and data quality
Boin A. T. and Hunter G. J. Do spatial data consumers really understand data quality information?, pp 215-224
Sadiq M. G. S. M. Z., Duckham M. and Hunter G. J. Modeling spatial variation in data quality using linear referencing, pp 225-235
van Oort P. A. J., Bregt A. K. and de Bruin S. Do users ignore spatial data quality?, pp 236-246
Positional uncertainty
Bley D. and Haller R. Checking the spatial accuracy of class boundaries with a varying range of accuracy requirements, pp 249-257
Casaca J. and Fonseca A. M. Modelling positional errors with isotropic random vector fields, pp 258-263
Casado M. L. Some basic mathematical constraints for the geometric conflation problem, pp 264-274
Imfeld S., Haller R. and Laube P. Positional accuracy of biological research data in GIS – a case study in the Swiss National Park, pp 275-280
Mas J.-F. Reducing positional error in spatio-temporal analyses, pp 281-285
Reis R., Egenhofer M. and Matos J. Topological relations using two models of uncertainty for lines, pp 286-295
Rennolls K. and Wang M. Enhancement of image-to-image co-registration accuracy using spectral matching methods, pp 296-305
Veronez M. R., Thum A. B. and de Souza G. C. A new method for obtaining geoidal undulations through Artifical Neural Networks, pp 306-316
Propagation of uncertainty
Bonin O. Sensitivity analysis and uncertainty analysis for vector geographical applications, pp 319-328
Lilburne L., Gatelli D. and Tarantola S. Sensitivity analysis on spatial models: a new approach, pp 329-338
Marinelli M., Corner R. and Wright G. A comparison of error propagation analysis techniques applied to agricultural models, pp 339-348
Posen P, Lovett A., Hiscock K., Reid B., Evers S. and Ward R. Error propagation in groundwater pesticide vulnerability modelling, pp 349-358
van der Linden T., Tiktak A., Heuvelink G. and Leijnse T. Spatial uncertainty analysis of pesticide leaching using a metamodel of PEARL, pp 359-366
de Wit A. and de Bruin S. Simulating space-time uncertainty in continental-scale gridded precipitation fields for agrometeorological modelling, pp 367-376
Spatial uncertainty modelling for categorical data
Bartoschek T., Painho M., Henriques R., Peixoto M. and Costa A. C. RENalyzer: a tool to facilitate the spatial accuracy assessment of digital cartography, pp 379-385
Hession S. L., Shortridge A. M. and Torbick N. M. Categorical models for spatial data uncertainty, pp 386-395
Néry F., de Sousa L., Marrecas P., Sousa R. and Matos J. Using spatially constrained clustering in land cover mapping, pp 396-405
Serra P., Moré G. and Pons X. Weighting fidelity versus classified area in remote sensing classification from a pixel and a polygon perspective, pp 406-416
Spatio-temporal analyses and uncertainty
Costa A. C. M. and Soares A. Identification of the inhomogeneities in precipitation time series using SUR models and the Ellipse test, pp 419-428
Halls J. N. and Kraatz L. Estimating error and uncertainty in change detection analyses of historical aerial photographs, pp 429-438
Lindenbergh R., Keshin M., van der Marel H. and Hanssen R. Kriging of spatial-temporal water vapor data, pp 439-448
Manzione R. L., Knotters M. and Heuvelink G. B. M. Mapping trends in water table depths in a Brazilian Cerrado area, pp 449-458
Propastin P., Muratova N. and Kappas M. Reducing uncertainty in analysis of relationship between vegetation patterns and precipitation, pp 459-468
Shokri T., Delavar M. R., Malek M. R. and Frank A. U. Modeling uncertainty in spatiotemporal objects, pp 469-478
Stochastic spatial simulation
Bastin L., Rollason J., Hilton A. C., Pillay D. G., Corcoran C., Elgy J., Lambert P., Worthington T., De P. and Burrows K. Assessing spatial clustering of MRSA with stochastic simulations, kernel estimation and SATSCAN, pp 481-489
Castrignano A., Buttafuoco G., Comolli R. and Ballabio C. Accuracy assessment of digital elevation model using stochastic simulation, pp 490-498
Delgado J., Soares A., Pérez J. L. and Carvalho J. Local error evaluation in DEM using direct sequential simulation (DSS) methodology, pp 499-509
Lilburne L., Hewitt A. and Ferriss S. Progress with the design of a soil uncertainty database, and associated tools for simulating spatial realisations of soil properties, pp 510-519
Wallerman J., Vencatasawmy C. P. and Bondesson L. Spatial simulation of forest using Bayesian state-space models and remotely sensed data, pp 520-530
Uncertainty in remotely sensed data
Brovelli M. A., Crespi M., Fratarcangeli F., Giannone F,. and Realini E. Accuracy assessment of High Resolution Satellite Imagery by leave-one-out method, pp 533-542
Brown K. M., Foody G. M. and Atkinson P. M. Deriving thematic uncertainty measures in remote sensing using classification outputs, pp 543-552
Çakir G., Keleş S., Sivrikaya F., Başkent E. Z. and Köse S. Determining the effects of different scanner and scanning resolutions on orientation errors in producing of Orthophotos, pp 553-556
Canters F., Chormanski J., Van de Voorde T. and Batelaan O. Effects of different methods for estimating impervious surface cover on runoff estimation at catchment level, pp 557-566
Carvalho J., Delgado-García J. and Soares A. Merging Landsat and SPOT digital data using stochastic simulation with reference images, pp 567-577
Couturier S., Mas J.-F., López E., Cuevas G., Vega A. and Tapia V. Accuracy assessment methodology for the Mexican national forest inventory: a pilot study in the Cuitzeo lake watershed, pp 578-587
Ge Y., Bai H. and Li D. A classification method for remotely sensed imagery by integrating with spatial structure information, pp 588-594
Günlü A., Kadioğullari A. I., Başkent E. Z., Güney D. and Buyuksalih G. The critical role of geographic information system (GIS) and remote sensing (RS) in forest site classification and mapping, pp 595-602
Hamm N. A. S., Atkinson P. M. and Milton E. J. On dealing with spatially correlated residuals in remote sensing and GIS, pp 603-613
Katila M. and Tomppo E. Sampling simulation on multi-source output forest maps – an application for small areas, pp 614-623
Quintanilha J. A. and Lee Ho L. A performance index developed by data envelopment analysis (DEA) to compare the efficiency of fire risk monitoring actions in municipalities of Brazilian Amazon region, pp 624-632
Schumann G., Black A., Cutler M., Henry J.-B., Hoffmann L., Matgen P. and Pfister L. Hydraulic and event knowledge to reduce the positional uncertainty in SAR flood images for improved flood model calibration and development, pp 633-642
Sivrikaya F., Keleş S., Çakir G., Başkent E. Z. and Köse S. Comparing accuracy of classified Landsat data with land use maps reclassified from the stand type maps, pp 643-652
Wongprayoon S., Vieira C. A. O. and Leach J. J. H. Assessing the thematic accuracy for coral reef classification, pp 653-662
Zhang J. and Sun J. Uncertainty characterization in remotely sensed land cover information, pp 663-672
Uncertainty in spatial data fusion
Hope S., Kealy A. and Hunter G. Improving positional accuracy and preserving topology through spatial data fusion, pp 675-684
Mostafavi M. A. Semantic similarity assessment in support of spatial data integration, pp 685-693
Olteanu, A.-M., Mustière S. and Ruas A., Matching imperfect spatial data, pp 694-704
Nunes V. B. and Caetano M. Mapping uncertainty in land cover characterization by comparison of land cover cartographies – a case study for Portugal, pp 705-715
Uncertainty in spatial decision making
Akyurek Z. and Okalp K. A fuzzy-based tool for spatial reasoning: a case study on soil erosion hazard prediction, pp 719-729
Dumont E., Kroeze C., Bakker E. J., Stein A. and Bouwman L. Development of a decision framework to identify appropriate spatial and temporal scales for modeling N flows, pp 730-739
Lowell K. E. and Benke K. K. Uncertainty and risk analysis in hydrological models for land-use managment, pp 740-749
Thum A. B., Cerioli G. and Veronez M. R. Analysis of spatial variability of PH, P and K in red latosoil cultivated in direct planting system, pp 750-759
Wright E. J. Fitness for use to support military decision making, pp 760-769
Using fuzzy set theory to characterise spatial uncertainty
Anile A. M., Spinella S. and Ostoich M. Best locations for river water quality monitoring sensors through fuzzy interpolation, pp 773-783
Fonte C. Conversion between the vector and raster data structures using Fuzzy Geographical Entities, pp 784-793
Morris A. and Petry F. E. UGML: an extension of GML to provide support for geographic objects with uncertain boundaries, pp 794-801
Visualisation of uncertainty in geographic data
Hengl T. and Toomanian N. Maps are not what they seem: representing uncertainty in soil-property maps, pp 805-813
Kardos J., Moore A. and Benwell G. Expressing attribute uncertainty in spatial data using blinking regions, pp 814-824
Pebesma E. J., Karssenberg D. and de Jong K. Dynamic visualisation of spatial and spatio-temporal probability distribution functions, pp 825-831
Poster Session
Afonso A. J. G., Dias R. A. F. C. and Teodoro R. F. S. IGeoE: Positional quality control in the 1/25000 cartography, pp 835-839
Afonso A. J. G., Dias R. A. F. C. and Teodoro R. F. S. IGeoE: Positional quality control with different rtk positioning methods, pp 840-846
Casado M. R., White S., Bellamy P., Dunbar M., Booker D. and Maddock I. Analysing the sensitivity of two variogram models for the characterisation of the spatial pattern of depth in rivers, pp 847-850
Gonçalves A. M. and Alpuim T. Precipitation measurement and the analysis of hydrological resources in a river basin, pp 851-860
Kadioğullari A. I., Başkent E. Z., Keleş S. and Günlü A. Analyzing the accuracy of spatial and temporal dynamics of land use pattern in Turkey: case study in Ínayet and Yenice forest planning units, pp 861-870
Nunes L. F. Accuracy improvement program for VMap1 to Multinational Geospatial Co-production Program (MGCP) using Artificial Neural Networks, pp 871-880
Teng S.-P., Cheng K.-S., Lo H.-C. and Chen Y.-K. Application of statistics to detection of green resources changes at Yangmingshan area using remote sensing imagery, pp 881-888
Veronez M. R., Thum A. B., Luz A. S. and da Silva D. R. Artificial Neural Networks applied in the determination of soil surface temperature – SST, pp 889-898
Yoshida M. and RPP-SEPMCL2002 Shipboard Scientist Team, Classification of spatial variation pattern for identifying pollution sources: a case study on sediment contamination in Bizerte Lagoon, Tunisia, pp 899-908