Our research in the media

Our article in Nature Ecology & Evolution got some media attention, both in the Danish and the international press.

Here is our article:

Brandt, M.; Rasmussen, K.; Peñuelas, J.; Tian, F.; Schurgers, G.; Verger, A.; Mertz, O.; Palmer, J. R. B.; Fensholt, R. Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa. Nature Ecology & Evolution 2017, 1, 0081.

Publishing in the open access journal Remote Sensing (MDPI)

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With our last article published, we are closing the special issue on land degradation for the open access journal Remote Sensing and I want to share some experiences here.

In total, 24 articles were submitted, 13 of them were published, 6 rejected without going to review, and 5 were rejected after review.

So we have an acceptance rate of 54%. Interestingly, this is very close to the 2013 statistics for all submissions, and it also reflects the overall quality of the submissions, which is average. The quality of the articles that were published in the end is ok, some are good, but rather not exceptional.

The interaction between us and the MDPI editorial staff was professional, smooth and efficient. Everything was prepared nicely for us and we could concentrate on the scientific part without any  managing aspects.

Having now 7 articles published in this journal (2 as first author) within the past 3 years, I can fully recommend publishing in Remote Sensing. Yes, the quality of the articles can not be compared with the leading journal “Remote Sensing of Environment” (here we have 4 articles now published within the past 2 years), and it is for sure easier to publish in Remote Sensing (RS) with less critical editors and reviewers. However, if you have an overall good quality article (not exceptional), there are several reasons for going for RS instead for the armada of Elsevier and Springer journals:

  • Open access: research should be available to everyone and not limited to rich countries and rich universities. Remote Sensing of Environment for example is not available at my former university, and in many German universities Elsevier journals are generally unavailable. Buying open access in these journals is possible but too expensive. Thousands of academics boycott Elsevier.
  • The authors of the article keep the rights on their research and are able to distribute their work freely.
  • Rapid processing, most of our 7 articles were published after around 2 months. The main reason is the professional editorial staff who do this work as full time job.
  • The articles are downloaded thousands of times and reach a wide audience.

One may argue the large number of average quality articles being published (being an online only journal, there are no issues and thus no article limit) swamps the scientific market and reduces importance of individual scientific work, but this is a general problem of science these days. One may also argue that the publisher MDPI is a company making money with each article they publish (and there is no limitation), so their aim is probably to publish as many articles as possible, and this is not beneficial for being critical. This might be true, however, in the end it’s up to the academic editors and the reviewers to decide if an article is published, not the company, and even the Nature and Science groups have their own mass publishing journals (Scientific Reports, Science Advances) now. Scientific publishing is about making profit.

Many people think that open access journals like RS are commercial companies making money (“you pay to get your paper published”), whereas articles published in Elsevier & co are non-commercial and real science. Here one should not forget that companies like Elsevier make billions of $$ profit each year, of which the reviewers see nothing and the editors do it as free time job being poorly paid. The universities pay absurd sums to make the articles available for their students, but many universities can not, and do not want to support this any more, but rather support the open access publishing by paying the publishing waves. In the end, this is much cheaper for the university and the article is freely available for everyone.

My personal recommendation: If you think you have an exceptional article dealing with remote sensing, there is no way around Remote Sensing of Environment, the reputation of this journal is untouchable. However, not every study we do has outstanding results, so if you do not want to wait more than a half year for a likely rejection, I personally can fully recommend Remote Sensing, the processing is rapid but still professional.

Brandt, M.; Tappan, G.; Diouf, A.A.; Beye, G.; Mbow, C.; Fensholt, R. Woody Vegetation Die off and Regeneration in Response to Rainfall Variability in the West African Sahel. Remote Sens. 2017, 9, 39.

New publications

Check out our new publications:

This paper raises awareness of data scale issues in environment migration research, for example the declining station network used in the CRU (3.2) dataset:

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With a case study of a Senegalese village we examine how (and if) migration patterns are linked with climate and environmental changes.Screenshot from 2016-07-14 14-19-10.png

This paper uses LASSO and Random Forest to select the best variables from a huge pool of indices to predict wood volume in Tadjikistan. Red Edge bands show to be of high value.

Screenshot from 2016-07-14 14-27-41.png

Spurious Correlations

By Lina Eklund & Martin Brandt

Correlations are a very famous and popular way to express relationships (and their strength) between two variables. Applications in environmental sciences span from relations of satellite based parameters with ground observations, to relationships between parameters like vegetation and precipitation. Furthermore, scientists use correlations to find linkages between totally different datasets of different scientific disciplines and spatial scales, e.g. migration and environment. However, many scientists blindly trust these statistical analyses and even low correlations are often interpreted in an awkward and very speculative way without questioning the results.

Too much reliance on statistical parameters can be dangerous, as you can have a strong correlation between two variables that are not related. This is shown in this website where, for example, the Per capita consumption of margarine (US) is correlated with Divorce rate in Maine at a correlation coefficient of 0.992558. How would you interpret such a relationship? Does this prove that married people shouldn’t eat margarine? It’s a nonsense correlation, these two variables simply happened to occur during the same years (the correlation was based on time, not space). In this relationship, the scale problems are quite obvious. First of all, the variables do not have the same spatial extent, even though they overlap in Maine. Also the temporal detail can be questioned. Many things happen during a year, so how would this correlation look at a finer temporal detail, for example monthly? We’re sure it would not be as strong.

Strong correlations can often be found between variables that are not directly linked, especially when the spatial and temporal details are coarse (e.g. nationwide, yearly).

The interpretation of statistical analysis outputs can be a challenge and therefore it is important to make sure that you know what you’re doing. Furthermore, the output values should be interpreted using common sense and an awareness of how scale issues might affect the results.

RGB compositions in QGIS

RGB compositions are very important in remote sensing studies, however, there doesn’t seem to be a function in QGIS. But there is an easy trick by creating a simple text file which stores the paths to images.

So go to “Raster – Miscellaneous – Build Virtual Raster (Catalog)”

pick the 3 raster layers, tick “Separate” and define an output file. That’s it. Now the text file can be loaded and the RGB displayed, like this RapidEye image with NDVI as red and bands 3 and 2 as green and blue.

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