review in Conservation Biology

a great review has been published in Conservation Biology. It is nicely addressing the scope of the book while also outlining what is missing – we are working on it to include also non-multispectral data – updates soon.

see here for the review and other interesting book reviews:

Remote Sensing and GIS for Ecologists Using Open Source Software. Wegmann, M., B. Leutner, and S. Dech. 2016. Pelagic Publishing, Exeter, U.K. 316 pp. $43.33 (paperback). ISBN 978-1-78427-022-3.

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.

MODIS sinusoidal grid download

Fig_11.3In Chapter 11 we pointed to the MODIS grid “to identify our tile, we use a shapefile with the MODIS tile system (, MODIS Sinusoidal GIS SHAPE files) and overlay it onto our study area (Figure 11.3). This shows that we need tile h13 v9.” However, the link does not work anymore, but you can download the MODIS sinusoidal grid from here  (Courtesy of Luca Delucchi, Fondazione Edmund Mach).

review by Harini Nagendra

Review by Harini Nagendra of our book:

“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.”
Harini Nagendra
Professor of Sustainability, Azim Premji University, Bangalore India

review by Allison Leidner, NASA Earth Science Division

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.”
Allison Leidner, Universities Space Research Association/NASA Earth Science Division

book review by H. deKlerk

“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.”

H. deKlerk, lecturer, Stellenbosch University, South Africa

book finally in store

Our book “Remote Sensing and GIS for Ecologists – Using Open Source software” is now available. First copies arrived and it looks pretty good. Great to have finally a copy on our desks after all the writing, testing and editing! We hope that you enjoy it as much as we do and that it helps you working with remote sensing and GIS in your research topics.

remote-sensing_gis_ecology_book_Wegmann_Leutner_Dech_2016You can order it here.

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 package R.

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. We 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.

check the table of content here:

updated RStoolbox version 0.1.4

The RStoolbox R package has been updated after some testing in courses and by colleagues. Please update your package using update.packages() or install the RStoolbox again.

New functions are:

  • new function `validateMap()` for assessing map accuracy separately from model fitting, e.g. after majority or MMU filtering
  • new function `getValidation()` to extract specific validation results of superClass objects (proposed by James Duffy)
  • new spectral index NDVIc (proposed by Jeff Evans)
  • new argument scaleFactor for `spectralIndices()` for calculation of EVI/EVI2 based on scaled reflectance values
  • implemented dark object subtraction radCor(..,method=’sdos’) for Landsat 8 data (@BayAludra, #4)

various changes were applied:

  • superClass() based on polygons now considers only pixels which have their center coordinate within a polygon
  • rasterCVA() now returns angles from 0 to 360° instead of 0:45 by quadrant (reported by Martin Wegmann and explained here)

  • improved dark object DN estimation based on maximum slope of the histogram in `estimateHaze` (@BayAludra, #4)

And some bugs fixed:

  • superClass() failed when neither valData or trainPartition was specified. regression introduced in 0.1.3 (reported by Anna Stephani)
  • spectralIndices() valid value range of EVI/EVI2 now [-1,1]
  • radCor() returned smallest integer instead of NA for some NA pixels
  • fix ‘sdos’ for non-contiguous bands in radCor (@BayAludra, #4)

First copies arrived

The first copy of our book arrived today and it looks pretty good. Great to have finally a copy on our desks after all the writing, testing and editing! We hope that you enjoy it as much as we do and that it helps you to work with remote sensing and GIS in your research topics.

Order your copy now!



Updated graphics for Change Vector Analysis

Graph outlining the Change Vector Analysis – updated version based on the graph from Chapter 9.

We explained in Chapter 9 among other change detection methods also the change vector analysis practically using the rasterCVA() command in the RStoolbox package, as well as outlined the approach graphically. During my last lecture on temporal and spatial remote sensing approaches I realized that the graphic needs some fixing as well as the RStoolbox function, moreover, certain explanations were missing. Hence, Benjamin Leutner adapted the rasterCVA() command and I tested it again and created new graphics explaining this approach for land cover change analysis.

The first (slightly modified) graph that is also in our book shows the general approach. Two bands for each year (e.g. the RED and NIR band) are taken and the changes in pixel values between these two years are shown as angle and magnitude.


We realized some things were missing:

  • the explanation what the angle actually means
  • a link of actual results and the xy-graph.
  • and an example using e.g. Tasseled Cap

In the following new figures we show the actual results of the land cover change vector analysis using band 3 and 4 of Landsat (E)TM for the study region used in our book and three angles and magnitudes of pixels values between 1988 and 2011.

Change Vector analysis explained on three change classes using the actual rasterCVA() output and band values.

In the second image we outline the meaning of the angle provided by rasterCVA() as well as the magnitude which is the euclidean distance of the pixel values between time-step 1 and time-step 2. The actual bands are shown on the x-axis (first band assigned in the command) and y-axis (second band):

Meaning of the angle values from a rasterCVA() output.


In another graph we just show the angle and magnitude of a forest to no-forest conversion between time-step 1 and time-step 2. This one if very similar to the above graph but we just assigned the start and end location of the change vector to actual band 3 and band 4 values:

Actual angle and magnitude using band 3 and band 4 for a forest to no-forest conversion pixel.


Similar results can be achieved using the Tasseled Cap output (greeness and wetness, from tasseledCap() command) as input for the change vector analysis. The map looks similar (we used as input the masked Landsat data in order to avoid the high magnitude values in the inundated areas for visualization purpose), but the actual change vector angle for the forest to no-forest conversion is different:

Change Vector Analysis results using wetness and greenness for two locations of forest to forest and forest to no-forest conversion.

Please update to the newest development version to access the updated RStoolbox functionality to redo these analysis. Please contact us for any recommendations concerning these graphs.