Accuracy 2006

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
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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