Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it.
This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made.
Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject:
* Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics
* Covers range of material from base overviews to specific applications
* Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS
Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.

Giles M. Foody is the editor of Uncertainty in Remote Sensing and GIS, published by Wiley.

Peter M. Atkinson is the editor of Uncertainty in Remote Sensing and GIS, published by Wiley.

List of Contributors vii

Foreword xi

Preface xvii

1 Uncertainty in Remote Sensing and GIS: Fundamentals 1
P. M. Atkinson and G. M. Foody

2 Uncertainty in Remote Sensing 19
C. E. Woodcock

3 Toward a Comprehensive View of Uncertainty in Remote Sensing Analysis 25
J. L. Dungan

4 On the Ambiguity Induced by a Remote Sensor's PSF 37
J. F. Manslow and M. S. Nixon

5 Pixel Unmixing at the Sub-pixel Scale Based on Land Cover Class Probabilities: Application to Urban Areas 59
Q. Zhan, M. Molenaar and A. Lucieer

6 Super-resolution Land Cover Mapping from Remotely Sensed Imagery using a Hopfield Neural Network 77
A. J. Tatem, H. G. Lewis, P. M. Atkinson and M. S. Nixon

7 Uncertainty in Land Cover Mapping from Remotely Sensed Data using Textural Algorithm and Artificial Neural Networks 99
A. M. Jakomulska and J. P. Radomski

8 Remote Monitoring of the Impact of ENSO-related Drought on Sabah Rainforest using NOAA AVHRR Middle Infrared Reflectance: Exploring Emissivity Uncertainty 119
D. S. Boyd, P. C. Phipps, W. J. Duane and G. M. Foody

9 Land Cover Map 2000 and Meta-data at the Land Parcel Level 143
G. M. Smith and R. M. Fuller

10 Analysing Uncertainty Propagation in GIS: Why is it not that Simple? 155
G. B. M. Heuvelink

11 Managing Uncertainty in a Geospatial Model of Biodiversity 167
A. J. Warren, M. J. Collins, E. A. Johnson and P. F. Ehlers

12 The Effects of Uncertainty in Deposition Data on Predicting Exceedances of Acidity Critical Loads for Sensitive UK Ecosystems 187
E. Heywood, J. R. Hall and R. A. Wadsworth

13 Vertical and Horizontal Spatial Variation of Geostatistical Prediction 209
A. Wameling

14 Geostatistical Prediction and Simulation of the Lateral and Vertical Extent of Soil Horizons 223
B. Warr, I. O. A. Odeh and M. A. Oliver

15 Increasing the Accuracy of Predictions of Monthly Precipitation in Great Britain using Kriging with an External Drift 243
C. D. Lloyd

16 Conditional Simulation Applied to Uncertainty Assessment in DTMs 269
J. SeÂneÂgas, M. Schmitt and P. Nonin

17 Current Status of Uncertainty Issues in Remote Sensing and GIS 287
G. M. Foody and P. M. Atkinson

Index 303