Remote Sensing and GIS for Ecologists: Using Open Source Software(Data in the Wild)

This is a book about how ecologists can integrate remote sensing and GIS in their daily work. It will allow ecologists to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. All practical examples in this book rely on OpenSource software and freely available data sets. Quantum GIS (QGIS) is introduced for basic GIS data handling, and in-depth spatial analytics and statistics are conducted with the software packages R and GRASS. Readers will learn how to apply remote sensing within ecological research projects, how to approach spatial data sampling and how to interpret remote sensing derived products. The authors discuss a wide range of statistical analyses with regard to satellite data as well as specialised topics such as time-series analysis. Extended scripts on how to create professional looking maps and graphics are also provided. This book is a valuable resource for students and scientists in the fields of conservation and ecology interested in learning how to get started in applying remote sensing in ecological research and conservation planning.

Table of Contents:
Introduction 1 Spatial Data and Software 2 Introduction to Remote Sensing and GIS 3 Where to Obtain Spatial Data? 4 Spatial Data Analysis for Ecologists: First Steps 5 Pre-Processing Remote Sensing Data 6 Field Data for Remote Sensing Data Analysis 7 From Spectral to Ecological Information 8 Land Cover or Image Classification Approaches 9 Land Cover Change or Change Detection 10 Continuous Land Cover Information 11 Time Series Analysis 12 Spatial Land Cover Pattern Analysis 13 Modelling Species Distributions 14 Introduction to the added value of Animal Movement Analysis and Remote Sensing Outlook and Acknowledgements Index

About the Author :
Martin Wegmann has a PhD in remote sensing focusing on time-series analysis on land cover change and fragmentation in Africa. He is an assistant professor at the Global Change Ecology Msc program at the University of Würzburg, Germany and runs courses in remote sensing analysis for biodiversity and conservation. Benjamin Leutner is a research assistant at the department of remote sensing at the University of Würzburg. He has extensive experience in geo-spatial analysis of remote sensing data using Open Source software.  Stefan Dech is director of the German Remote Sensing Data Center (DFD) since 1998, and current spokesman of the Earth Observation Center (EOC) at the German Aerospace Center (DLR). Since 2001 he has held the Chair for Remote Sensing at the Institute of Geography and Geology of the University of Würzburg. 

Review :
Satellite remote sensing and GIS were once the preserves of a small number of well-financed groups, but the field has been democratised by open-source software. QGIS and R are covered by this textbook aimed at a practitioners who want to know how to obtain, process and analyse remotely sensed data. It provides excellent guidance on designing studies and recognising both the potential and limitations of remotely sensed data. Later chapters cover common ecological applications, including distribution modelling and land cover pattern analysis. The book is printed in high quality with numerous colour figures. It would make an excellent companion to a workshop. The book has a clearly stated aim: “to make remote sensing data and tools more accessible to ecologists”. The structure follows a logical and straightforward outline, which not only gives a comprehensive overview on the applications of remote sensing in ecology, but it proposes also an organized workflow useful not only for newcomers of RS & GIS. Each chapter adds a step in a realistic sequence often demanded by real case studies. I tested some of the proposed procedures with my collaborators and we found them quite effective and easily applicable in practice. We can then state that the authors met the declared aim. We appreciated the choice of open source software (OSS) declared in the book's subtitle. Basing the methodology on open source tools such as QGIS and R is a great advantage and it is coherent with the current era of free Earth Observation data. This gives the opportunity to approach ecological analyses at no cost avoiding the frustration of expensive commercial licenses that may quickly expire. Furthermore, the specific choice of QGIS and R as reference tools is justified by large and active communities, which can promptly assist users (especially newbies) on online forums, a great resource in alternative to usual help pages. In particular, the detailed and straightforward introduction to the R raster and remote sensing toolbox (RStoolbox) can be of great help. RStoolbox although with great capabilities and supported by a nice graphical potential, has a steep learning curve (as R in general) and was lacking of an easy introduction material with concrete examples. Therefore, readers that reach the end of the book following the exercises proposed will overcome most of the problems encountered in the learning phase of script writing. Moreover, many script samples are given directly on the book and more complex ones can be accessed online. This allows the creation of a nice library of example scripts that can be easily adapted to case to case analysis. One of the great advantages of using R and its scripts is the transparency and reproducibility of algorithms. This is the case for the examples provided for classification of land cover, for analysis of time series and land cover patterns and for species distribution modelling. Many tips and tricks are taught in the exercise proposed. They are useful to understand how the software work and how to be efficient while working with them. This is important especially when working with a large amount of raster in R or simply on a wide area of interest, such as for time series or land cover patterns analysis, which could require an amount of RAM memory or even disk space, exceeding the ones available. The methodologies explained help to save computational resources, disk space and increase efficiency in terms of time. Remote Sensing and GIS for Ecologists could become an essential undergraduate-level textbook, but it is also a guide to practising ecologists who want to broaden their toolkit. We recommend this book not only as an interesting and informative guide to remote sensing concepts, but also as a vehicle to quickly delve into hands-on processing and analysis of remote sensing data to answer many questions relevant to landscape ecologists.