Spatial Accuracy Assessment: Land Information Uncertainty in Natural ResourcesKim Lowell, Annick Jaton Spatial technologies such as GIS and remote sensing are widely used for environmental and natural resource studies. Spatial Accuracy Assessment provides state-of-the-science methods, techniques and real-world solutions designed to validate spatial data, to meet quality assurance objectives, and to ensure cost-effective project implementation and completion. If you use GIS, remote sensing and other spatial mapping technologies for resource management, land use planning, engineering or environmental studies, this vital reference will save you time and money. |
Contents
Part I | 11 |
An Agenda for Change N R Chrisman | 27 |
Part II | 33 |
Accept in Our Decisions? A Agumya and G J Hunter 3+35 | 45 |
Monte Carlo Modeling of Uncertainty and the Provision | 53 |
Accuracy Assessment and Hydrological Simulation Sensitivity | 61 |
A Case Study in the Great Barrier Reef Lagoon A Lewis | 71 |
ssessing Uncertainty in Modeling Coastal Recession Due to Sea Level Rise | 79 |
Part VI | 217 |
Evaluation of a Procedure for Line Generalization of a Statewide Land Cover Map R S Dzur | 227 |
An Experimental Software for Regularization of Lines Surveyed | 233 |
Regional Predictions | 247 |
The Effects of DEM Generalization Methods on Derived Hydrologic Features D B Gesch | 255 |
Estimation of LandCover Proportions from Aggregated MediumResolution Satellite Data | 263 |
Part VII | 271 |
Local Reduction of Systematic Error in 712 Minute DEMs by Detecting Anisotropy | 281 |
ensitivity of a Quantitative SoilLandscape Model to the Precision of | 89 |
Part III | 97 |
Characterizing Local Spatial Uncertainty in the Optimization | 105 |
Describing Uncertainty in Categorical Maps Using Correlated Categorical Data | 113 |
Incapsulating Simulation Models with Geospatial Data Sets | 123 |
ormulation and Test of a Model of Positional Distortion Fields | 131 |
ntroducing the Concept of Reliability in Spatial Data M Azouzi | 139 |
Part IV | 145 |
isualization of Fuzzy Spatial Information in Spatial DecisionMaking | 151 |
Ise of Variograms to Represent Spatial Uncertainty of Geographic Linear Features F Vauglin | 157 |
Assessing and Visualizing Accuracy During 3D Data Capture at Digital Photogrammetric | 165 |
Part V | 173 |
Toward a Theory of Vector Error Characterization and Propagation G Edwards | 183 |
Super Ground Truth as a Foundation for a Model to Represent | 189 |
Rigorous Geospatial Data Uncertainty Models for GISS | 195 |
Application of a New Model of Vector Data Uncertainty | 203 |
Implementing an ObjectOriented ErrorSensitive GIS M Duckham | 209 |
Improving Air Temperature Interpolation Using Satellite Data A Viau and Y Huang | 299 |
Part VIII | 307 |
Estimating the Proportion | 319 |
SpatioTemporal Prediction of Level 3 Data for NASAs Earth Observing System | 331 |
Part IX | 339 |
Assessment of the Spatial Structure and Properties of Existing Ecoregionalization | 349 |
The Development of a Decision Model | 357 |
Monitoring Defoliation of Forest Trees by Means of LargeScale Digital Image Processing | 365 |
Spatial Monitoring Protocol to Optimize the Monitoring of Forest Entities with Remote | 383 |
Designing an Accuracy Assessment for a USGS Regional Land Cover Mapping Program | 393 |
Uncertainty in Automatically Sampled Digital Elevation Models M J P M Lemmens | 399 |
Quality Control and Validation of PointSourced Environmental Resource Data | 409 |
Preserving Spatial and Attribute Correlation in the Interpolation of Forest Inventory Data | 419 |
Mapping Forest Site Potential at the Local and Landscape Levels | 431 |
| 445 | |
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Common terms and phrases
algorithm analysis application approach Arc/Info attribute band boundary calculated cell chapter classification clustering co-kriging complex components computed correlation data quality data set database defined DEMs derived developed Digital Elevation Models distance distribution ecotope eigenvectors Environmental error band error propagation estimates example Figure forest function fuzzy Fuzzy Sets Geographic Information Geographical Information Systems Geostatistics global Goodchild grid input interpolation interpretation land-cover linear mean measure meters method object output overlay parameters Photogrammetric Photogrammetric Eng pixel points polygon positional predicted problem procedure proportions random raster reef region regression Remote Sensing represent risk RMSE sample satellite scale scatterplots semivariance semivariogram simulation slope soil type spatial autocorrelation spatial data spatial resolution spatial uncertainty species spectral standard deviation statistics structure study area surface survey Table tainty techniques terrain tion uncer uncertainty model variables variance variogram vector visual weight