“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 is brilliant – a real gem. It contains some of the best descriptions I’ve seen of planning a GIS/RS research project, steps to follow, statistics and approaches used in species modelling and remote sensing classification. […] I’m definitely going to recommend it to my students. It is particularly clear and focussed for those interested in using spatial analyses for conservation management questions.”
“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.”
“This massive guidebook provides an impressive integration of theoretical concepts of remote sensing, GIS and spatial analysis with practical approaches using a number of field examples, available as free datasets for people to practice on, using open source software throughout for maximum accessibility. From how to begin with spatial data sampling, all the way through to the final creation of publishable maps and graphics, the book is an invaluable one-stop resource for ecologists, who are now increasingly utilising the power of spatial datasets for research, conservation practice and policy.”
“The insights that remote sensing and GIS can provide to ecologists offer an amazing opportunity to advance research, but the learning curve to use such tools can be steep. This book helps the reader wade through what could feel like an overwhelming amount of information to practically apply remote sensing and GIS to ecological questions. Importantly, this book enables the reader to learn a high-level concept and become familiar with the overall language used in the discipline, and then zoom in to the nuts and bolts of how to actually execute an analysis. Consequently, the book will be a valuable resource to ecological researchers, particularly because of the focus on open source software.”
“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”
“This book to open methods created by the ongoing geospatial revolution to ecologists, who could then use remote-sensing data more widely for local- or global-scale research. It is a textbook, not a scientific review, and, as is clearly stated, aims to be a primer. The authors only assume basic computer skills but no theoretical knowledge of geographic information systems (GIS), and the context is platform independent. This sounds like a dream for many scientists, but it does come with a limitation. From the vast opportunities offered by remote sensing and GIS, this book discusses only analysis of multispectral satellite remote-sensing data at regional scale, and even that at an introductory level. Within this scope, however, it is a rather comprehensive guide and includes not only image handling, processing, and classification but also collection of reference data, accuracy analyses, and more ecology-focused applications such as species distribution modeling and animal-movement analysis. As a textbook, it does not focus on the current state of the art or the most recent developments; rather, it introduces the tools and methods that are routinely applied nowadays. The book relies on open-source software. For ecologists who are usually not programmers, the main advantage of this is that (at least) the software is free. The book also points the user toward freely available data sets and describes workflows that are uncomplicated and robust. The brave step this textbook takes is to introduce R and command-line or script-based GIS processing. Although working without a graphical user interface may involve a steep learning curve, it is clearly worth the investment because it allows an iterative approach and processing of large data sets. The authors successfully balance usefulness to a novice and theoretical correctness and introduce what may be called remote-sensing common sense through many practical tips and do’s and don’ts. Still, this book will not save one from experimenting on a trial and error basis. The authors stress there is no single method that can be recommended and that many have to be tested to find the appropriate fit. All the examples are demonstrated on the same data set, which tempts the reader to use the online resources provided. Despite its challenging scope, it is not too long, and the chapters are concise enough to work as self-contained exercises. Remote Sensing and GIS for Ecologists could become an essential undergraduate-level textbook, but it is also a guide to practicing ecologists who want to broaden their toolkit, especially ecologists who work in interdisciplinary teams.”