Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications

" Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications" is a book authored by Michael Goodchild that explores the issue of spatial uncertainty in ecological research and its implications for remote sensing and GIS applications. The book is a comprehensive guide to the latest methods and techniques for dealing with spatial uncertainty in ecology, with a focus on how these methods can be used in conjunction with remote sensing and GIS technologies.

The book starts by introducing the concept of spatial uncertainty and its relevance to ecological research. It then goes on to discuss the different sources of spatial uncertainty, including errors in data collection, processing, and analysis. The book also explores the different types of spatial uncertainty, including positional uncertainty, attribute uncertainty, and temporal uncertainty.

One of the key strengths of the book is its focus on the practical applications of spatial uncertainty in ecological research. The book provides a detailed overview of the different methods and techniques that can be used to quantify and model spatial uncertainty, including Monte Carlo simulation, fuzzy logic, and Bayesian networks. The book also includes a range of case studies that demonstrate how these methods can be applied in practice.

In addition to its focus on spatial uncertainty, the book also explores the latest developments in remote sensing and GIS technologies and their applications in ecology. The book provides a comprehensive overview of the different types of remote sensing data that are available, including satellite imagery, airborne lidar, and ground-based sensors. It also explores the latest advances in GIS software and how these can be used to integrate different types of spatial data.