Uncertainty In Remote Sensing And Gis

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.


Foreword.


Preface.


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


Uncertainty in Remote Sensing (C. E. Woodcock).


Toward a Comprehensive View of Uncertainty in Remote Sensing Analysis (J. L. Dungan).


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


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


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


Uncertainty in Land Cover Mapping from Remotely Sensed Data using Textural Algorithms and Artificial Neural Networks (A. M. Jakomulska and J. P. Radomski).


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


Land Cover Map 2000 and Meta-data at the Land Parcel Level (G. M. Smith and R. M. F uller).


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


Managing uncertainty in a Geospatial Model of Biodiversity (A. J. Warren, M. J. Collins, E. A. Johnson and P. F. Ehlers).


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


Vertical and Horizontal Spatial Variation of Geostatistical Prediction (A. Wameling).


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


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


Conditional Simulation Applied to Uncertainty Assessment in DTMs (J. Sénégas, M. Schmitt and P. Nonin).


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


Index.