Forests hold a vast amount of Earth’s terrestrial carbon and play an important role in offsetting anthropogenic emissions of fossil fuels. Since 2015, the world’s tropical forests can be observed regularly at unprecedented 6 to 12-day intervals thanks to the Copernicus Sentinel-1 mission.

Millions of gigabytes of synthetic aperture radar (SAR) data are acquired both day and night, regardless of cloud cover, haze, smoke, or aerosols, allowing deforestation and forest degradation to be monitored at least biweekly.

The challenge, however, lies in finding adequate methods to extract meaningful indicators of forest loss from the vast amounts of incoming radar data, such that anomalies in the time series can be regularly and consistently detected across tropical forests.

Such forest-monitoring methods should be transparent and easily understandable to the wider public, enabling confidence in their use across various public and private sectors.

The Sentinel-1 for Science: Amazonas project presents a simple and transparent approach to using Sentinel-1 satellite radar imagery to estimate forest loss. The project uses a space-time data cube design (also known as StatCubes), where statistical information relevant to identify deforestation is extracted at each point in the radar time series. 

With this approach, the project demonstrates the use of Sentinel-1 data to create a dynamic deforestation analysis over the Amazon basin. The team was able to detect forest loss of over 5.2 million hectares from 2017 to 2021, which is roughly the size of Costa Rica.

Neha Hunka, Remote Sensing Expert at Gisat, commented, “What we are seeing from space is over a million hectares of tropical moist forests disappearing each year in the Amazon basin, with the worst year being 2021 in Brazil. We can track these losses and report on them transparently and consistently every 12 days henceforth.”

Billions of pixels from the Sentinel-1 satellites from early-2015 to December 2021, each representing a 20 x 20 m of the forest, are harmonized under the StatCubes design, and a simple thresholding approach to detect forest loss is demonstrated in the first version of the results.

The largest challenge in the project was the vast amount of data handling and processing. The team used several user-friendly software tools to access the data efficiently – processing over 450 TB of data to create the forest loss maps.

Anca Anghelea, Open Science Platform Engineer at ESA, added, “By providing open access data and code through ESA’s Open Science Data Catalogue, and openEO Platform, we aim to enable researchers around the world to collaborate and contribute to the advancement of knowledge about our global forests and the carbon cycle.

"Thus, in the last phase of the project, a key focus will be on Open Science, reproducibility, long-term maintenance, and evolution of the results achieved in the Sentinel-1 for Science: Amazonas Project.”    

Following on from the project, the next goal is to achieve a product of carbon loss from land cover changes, working together with ESA’s Climate Change Initiative team – a goal that will contribute to ESA’s Carbon Science Cluster.

The current results of the project are now available by clicking here. Sentinel-1 for Science Amazonas is implemented by a consortium of four partners - Gisat, Agresta, the Norwegian University of Life Sciences, and the Finnish Geospatial Research Institute. The team uniquely combines complementary and strong backgrounds in forestry and carbon assessments, multi-temporal SAR analysis and data fusion, and large-data processing capabilities.

As the evolution of standard microchips is coming to an end, scientists are looking for a revolution. The big challenges are to design chips that are more energy efficient and to design devices that combine memory and logic (memristors). Materials scientists from the University of Groningen, the Netherlands, described in two papers how complex oxides can be used to create very energy-efficient magneto-electric spin-orbit (MESO) devices and memristive devices with reduced dimensions. 

The development of classic silicon-based computers is approaching its limits. To achieve further miniaturization and to reduce energy consumption, different types of materials and architectures are required. Tamalika Banerjee, Professor of Spintronics of Functional Materials at the Zernike Institute for Advanced Materials, University of Groningen, is looking at a range of quantum materials to create these new devices. "Our approach is to study these materials and their interfaces, but always with an eye on applications, such as memory or the combination of memory and logic." 

More efficient

The Banerjee group previously demonstrated how doped strontium titanate can be used to create memristors, which combine memory and logic. They have recently published two papers on devices "beyond CMOS," the complementary metal oxide semiconductors which are the building blocks of present-day computer chips.

One candidate to replace CMOS is the magneto-electric spin-orbit (MESO) device, which could be 10 to 30 times more efficient. Several materials have been investigated for their suitability in creating such a device. Job van Rijn, a Ph.D. student in the Banerjee group, is the first author of a paper in Physical Review B published in December 2022, describing how strontium manganate (SrMnO3 or SMO for short) might be a good candidate for MESO devices. ‘It is a multiferroic material that couples spintronics and charge-based effects,’ explains van Rijn. Spintronics is based on the spin (the magnetic moment) of electrons.

Banerjee: "The magnetic and charge orderings are coupled in this material, so we can switch magnetism with an electric field and polarization with a magnetic field." And, importantly, these effects are present at temperatures close to room temperature. Van Rijn is investigating the strong coupling between the two effects. "We know that ferromagnetism and ferroelectricity are tuneable by straining a thin SMO film. This straining was done by growing the films on different substrates."

Strain

Van Rijn studies how strain induces ferroelectricity in the material and how it impacts the magnetic order. He analyzed the domains in the strained films and noticed that magnetic interactions are greatly dependent on the crystal structure and, in particular, on oxygen vacancies, which modify the preferred direction of the magnetic order. ‘Spin transport experiments lead us to the conclusion that the magnetic domains play an active role in the devices that are made of this material. Therefore, this study is the first step in establishing the potential use of strontium manganate for novel computing architectures.’

On 14 February, the Banerjee group published a second paper on devices ‘beyond CMOS’, in the journal Advanced Electronic Materials. Ph.D. student Anouk Goossens is the first author of this paper on the miniaturization of memristors based on niobium-doped strontium titanate (SrTiO3 or STO). ‘The number of devices per unit surface area is important,’ says Goossens. "But some memristor types are difficult to downscale." 

Goossens previously showed that it was possible to create ‘logic-in-memory’ devices using STO. Her latest paper shows that it is possible to downscale these devices. A common problem with memristors is that their performance is negatively impacted by miniaturization. Surprisingly, making smaller memristors from STO increases the difference between the high and the low resistance ratio. ‘We studied the material using scanning transmission electron microscopy and noticed the presence of a large number of oxygen vacancies at the interface between the substrate and the device’s electrode’, says Goossens. "After we applied an electric voltage, we noticed oxygen vacancy movement, which is a key factor in controlling the resistance states."

New design

The conclusion is that the enhanced performance results from edge effects, which can be bad for normal memory. But in STO, the increased electric field at the edges supports the function of the memristor. "In our case, the edge is the device," concludes Goossens. ‘In addition, the exact properties depend on the amount of niobium doping, so the material is tuneable for different purposes."

In conclusion, both papers published by the group show the way toward novel supercomputing architectures. Indeed, the STO memristors have inspired colleagues of Goossens and Banerjee at the University of Groningen Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence and CogniGron (Groningen Cognitive Systems and Materials Center), who have already come up with a new design for memory architecture.

"This is exactly what we are working for," says Banerjee. We want to understand the physics of materials and how our devices work and then develop applications.’ Goosens: ‘We envision several applications and the one we are looking at is a random number generator that works without an algorithm and is therefore impossible to predict."