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).

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.

change in EarthExplorer Landsat CDR

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Book submitted

book_remote_sensing_gis_ecologists_wegmann_leutner_dechWe finally submitted our book “Remote Sensing and GIS for Ecologists” to the publisher. It took longer than anticipated and we learned a lot. Looking forward to the printed version. More details how to order and the expected publishing date can be found at

we will post news and updates about the book on our news page, as well as updates of related software packages.Remote_Sensing_GIS_Ecology_book_preorder