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

Michael Goodchild is a geographer and a pioneer in the field of geographic information science (GIS). He was born on February 24, 1944, in Surrey, England, and obtained his PhD in geography from the University of Bristol in 1969.

Goodchild is known for his research on GIS, spatial analysis, and remote sensing. He has authored or co-authored over 15 books and more than 500 articles, and has received numerous awards for his contributions to the field of geography and GIS.

In addition to his academic work, Goodchild has been involved in various organizations and initiatives related to GIS, including serving as the editor of the journal "Geographical Analysis" and as the founding chair of the National Center for Geographic Information and Analysis (NCGIA) in the United States.

Goodchild is currently a professor emeritus of geography at the University of California, Santa Barbara, and continues to be active in research and teaching.

Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications pdf by Michael Goodchild

" 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.