Remote Sensing and GIS for Ecologists. Using Open Source Software.

Pitched as a textbook, rather than as a source book providing standards for others to follow, Remote Sensing and GIS for Ecologists seeks to provide readers with the tools and examples that will make remote sensing data more accessible for ecological studies. It is not a definitive guide to remote sensing data, nor does it cover the full range of alternative methods and approaches for analysing such data, but it will get you started using QGIS and R to process, manipulate, visualise and analyse remote sensing data. Both QGIS and R are open source software, hence the book’s subtitle, so the reader will find additional advice and support online.

The book begins with a short introductory chapter written by Nathalie Pettorelli, which looks at some of the opportunities for using Satellite Remote Sensing (SRS) to inform conservation biology. Pettorelli’s example explore the use of SRS to define and monitor Protected Areas, inform reintroduction programmes, predict the impacts of a changing climate and study invasive alien species.

The following three chapters also provide introductory information, examining the origins and structure of spatial data and the software available to study it, together with guidance on where to source such datasets. Having effectively dealt with the fundamentals and theoretical background of remote sensing and GIS, chapter four gets into the meatier material on carrying out the analysis of spatial data. There is a fair bit of detail in this section on using the two OSS packages with, as you would expect in a textbook, a good amount of code using worked examples. The emphasis here is on first steps, preparing the reader for the chapters which follow and delivering a good grounding in how to pre-processing (Chapter 5) SRS and field collected data (Chapter 6).

At its heart, the analysis of remote sensing data is about interpreting the spectral information contained within the data. The spectral signal carries significantly more information about the land surface than is available to the human eye, and the object of the analysis is often to transform the original information into a series of spectral indices. Of the various indices it is the Vegetation Indices that are used most frequently by ecologists, offering the opportunity to formally test changes in land cover, either over time or across spatial scales. Chapter 7 looks at the Vegetation Indices in detail, explaining for example the NDVI (Normalised Difference Vegetation Index) and how to use PCA techniques to tackle the redundancy often seen in the spectral signal – band to band correlation in multispectral imagery is high.

This chapter leads neatly on to chapters on image classification and land cover approaches, and on change detection and land cover change. Again, the approach is centred on categorical representations of land cover. However, it is more often the case that the cover types present within a landscape do not sit within neat, sharply delimited boundaries. Instead, the changes from one cover type to another are often continuous and this requires a different approach, such as fractional cover analyses (chapter 10). Chapter 11 introduces time series analysis, where SRS datasets of the same area, captured at different points in time, can be used to examine landscape change.

Up until this point in the book, the emphasis has been on extracting ecologically relevant information based on the spectral signal. However, there are occasions where differentiating between habitat components that share a similar spectral signal – for example a natural forest and a piece of plantation woodland. In such cases, it is possible to use variation within the spectral signal to identify the different habitat components – a plantation woodland, comprised of a single tree species all planted at the same time, will show less variability in its spectral signal than a natural forest block. Texture analysis, the approach for examining such variation, is covered in Chapter 12, where Duccio Rocchini is the lead author.

The final two chapters look at modelling species distributions and tracking the movements of individuals through time, both of which might have benefited from more space within the book (or books in their own right), although the treatment provides an excellent practical overview in both cases.

Overall, this book achieves what it sets out to deliver. The information presented is clear, well illustrated by coding examples and accompanied by useful figures. It is a textbook and one that is likely to be well used.

Book reviewed by Mike Toms

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