New open-source software supports land-cover monitoring

By Victor Maus, IIASA Ecosystems Services and Management Program

Nowadays, satellite images are an abundant supply of data which we can use to get information about our planet and its changes. Satellite images can, for example,  help us detect an approaching storm, measure the expansion of a city, identify deforested areas, or estimate how crop areas change over time. Usually, we are interested in extracting information from large areas, for example, deforestation in the Amazon Rainforest (5.5 million km², around 15 times the area of Germany). It would be challenging for us to monitor and map such vast areas without combining satellite images with automated and semi-automated computer programs.

Aerial view of the Amazon Rainforest, near Manaus, Brazil. Monitoring deforestation in the Amazon is difficult because the area is massive and remote. ©Neil Palmer | CIAT

To address this problem, I developed — along with my colleagues Gilberto Camara from the Brazilian National Institute for Space Research and Marius Appel and Edzer Pebesma from the University of Münster, Germany — a new open source software to extract information about land-cover changes from satellite images. The tool maps different crop types (e.g., soybean, maize, and wheat), forests, and grassland, and can be used to support land-use monitoring and planning.

Our software, called dtwSat, is open-source and can be freely installed and used for academic and commercial purposes. It builds upon on other graphical and statistical open-source extensions of the statistical program R. Adding to that, our article in press in Journal of Statistical Software is completely reproducible and provides a step-by-step example of how to use the tool to produce land-cover maps. Given that we have public access to an extensive amount satellite images, we also get much benefit from tools that are openly available, reproducible, and comparable. These, in particular, can contribute to rapid scientific development.

The software dtwSat is based on a method widely used for speech recognition called Dynamic Time Warping (DTW). Instead of spoken words, we adapted DTW to identify ‘phenological cycles’ of the vegetation. These encompass the plants’ life cycle events, such as how deciduous trees lose their leaves in the fall.  The software compares a set of phenological cycles of the vegetation measured from satellite images (just like a dictionary of spoken words) with all pixels in successive satellite images, taken at different times. After comparing the satellite time series with all phenological cycles in the dictionary, dtwSat builds a sequence of the land-cover maps according to similarity to the phenological cycles.

The series of maps produced by dtwSat allows for land-cover change monitoring and can help answer questions such as how much of the Amazon rainforest has been replaced with soy or grass for cattle grazing during the last decade? It could also help study the effects of policies and international agreements, such Brazil’s Soy Moratorium, where soybean traders agreed not to buy soy from areas deforested after 2006 in the Brazilian Amazon. If soy farming cannot expand over areas deforested after 2006, it might expand to areas formerly used for cattle grazing deforested before 2006, and force the cattle grazing farmers to open new areas that have been cleared more recently. Therefore, besides monitoring changes, the land-cover information can help better understand direct and indirect drivers of deforestation and support new land-use policy.

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Further info: dtwSat is distributed under the GPL (≥2) license. The software is available from the IIASA repository PURE Precompiled binary available from CRAN at

dtwSat development version available from GitHub at


Maus V, Camara G, Appel M, & Pebesma E (2017). dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R. Journal of Statistical Software (In Press).

Maus, V, Camara, G, Cartaxo, R, Sanchez, A, Ramos, FM, & de Queiroz, GR (2016). A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (8): 3729–39.

This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Is open science the way to go?

By Luke Kirwan, IIASA open access manager

At this year’s European Geosciences Union a panel of experts convened to debate the benefits of open science. Open science means making as much of the scientific output and processes publicly visible and accessible, including publications, models, and data sets.

Open science includes not just open access to research findings, but the idea of sharing data, methods, and processes. ©PongMoji | Shutterstock

In terms of the benefits of open science the panelists—who included representatives from academia, government, and academic publishing—generally agreed that openness favors increased collaboration and the development of large networks, especially in terms of geoscience data, which improves precision in the interpretation of results. There is evidence that sharing data and linking to publications increases both readership and citations. A growing number of funding bodies and journals are also requiring researchers to make the data underlining a publication as publicly available as possible. In the context of Horizon 2020, researchers are instructed to make their data ‘as open as possible, as closed as necessary.’

This statement was intentionally left vague, because the European Research Council (ERC) realized that a one size fits all approach would not be able to cover the entirety of research practices across the scientific community, said Jean-Paul Bourguignon, president of the ERC.

Barbara Romanowicz from Collège de France and Institut de Physique du Glove de Paris also pointed to the need for disciplines to develop standardized metadata standards and a community ethic to facilitate interoperability. She also pointed out that the requirements for making raw data openly accessible are quite different to those for making models accessible. These problems require increased resources to be adequately addressed.

Roche DG, Lanfear R, Binning SA, Haff TM, Schwanz LE, Cain KE, Kokko H, Jennions MD, Kruuk LEB (2014). Troubleshooting public data archiving: suggestions to increase participation. PLOS Biology. 12 (1): e1001779. doi:10.1371/journal.pbio.1001779.

Playing devil’s advocate, Helen Glaves from the British Geological Survey pointed to several areas of potential concern. She questioned whether the costs involved in providing long-term preservation and access to data are the most efficient use of taxpayers money. She also suggested that charging for access could be used to generate revenues to fund future research. However, possibly a more salient concern for researchers that she raised was  the fear of scientists that making their data and research available in good faith, could allow their hard work to be passed off by another researcher as their own.

Many of these issues were raised by audience members during the questions and answer session. Scientists pointed out that research data involved a lot of hard work to collate, they had concerns about inappropriate secondary reuse, jobs and research grants are highly competitive. However, the view was also expressed that paying for access to research fundamentally amounts to ‘double taxation’ if the research has been funded by public money, and that even restrictive sharing is better than not sharing at all. It was also argued that incentivising sharing through increased citations and visibility would both help encourage researchers to make their research more open and aide researchers in the pursuit of grants or research positions. To bring about these changes in research practices will involve investing in training the next generation of scientists in these new processes.

Here at IIASA we are fully committed to open access and in the library, we assist our researchers with any queries or issues they may have with widely sharing their research. As well as improving the visibility of research publications through Pure, our institutional repository, we can also assist with making research data discoverable and citable.

A video of the discussion is available on YouTube.

This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.