How do you personally and professionally relate to water?
Personally, living in Switzerland, known for being the water castle of Europe, I have a strong link with water. Geneva is the biggest city around the Léman Lake which is the largest lake in Europe and a place where we all enjoy going during all the seasons to enjoy is this beautiful area.
Consequently, this has somehow also influenced my work as a researcher at the Institute for Environmental Sciences and head of the unit at UNEP. I’m aiming to help monitor and protect this essential natural resource through satellite Earth Observations.
Could you tell us a bit more about your current work, your latest project, or your proudest professional moment?
I’m currently leading the Swiss Data Cube project which is a unique Analysis Ready Data archive of Switzerland allowing to analyze and generate decision-ready information products for various stakeholders at different scales (e.g., communes, cantons, national). With this tool, our team is aiming to help gain knowledge from almost 40 years of satellite data, getting a better understanding of how Switzerland’s landscape has evolved, and ultimately how to efficiently and effectively protect our fragile environment.
I’ve been very honored to be invited to the World Economic Forum in Davos in 2020 to present the Swiss Data Cube during a side event organized by the University of Geneva and the Swiss government. That was very impressive, and I was particularly proud of the team effort to show how space technologies can be helpful for environmental monitoring.
How has the use of satellite data to support the decision-making process related to environmental issues evolved over the past years?
I have the feeling that thanks to the Sustainable Development Goals (SDGs), decision-makers are starting to better understand the potential benefit they may have using satellite EO data. With the advent of many key technologies (e.g., Data Cube, Cloud processing, Machine Learning) that have emerged in recent years allowing to efficiently extract useful information from the continuously increasing volume of data captured by satellite this shift has become even more possible. For example, we are currently collaborating with archeologists from the State of Valais in the Alps, and the information we generate on Snow Cover evolution can help them to identify and prioritize areas of interventions to protect archeological remains and consequently take better decisions to protect these invaluable archives of our common history.
Looking at the contributions of Digital Earth to the SDGs, which ones have a link to water and what targets and indicators could benefit from the use of EO data that are not currently doing so? How can the international community benefit from Digital Earth? What steps need to be taken to harness its full potential?
This is a good question, and I would like to turn you to a very recent publication that we wrote with many estimated colleagues involved in the Digital Earth (DE). This publication is looking at the past, present and future of the Digital Earth vision, what are the technologies that have strongly influenced and shaped this vision, and how can we benefit from DE to support policy frameworks like the SDGs.
The concept of Digital Earth (DE) was formalized by Al Gore in 1998. At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary. Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society. This creates new opportunities and challenges for the realization of DE.
Besides that, I strongly believe that an important step to fully harness the information power of satellite data, concerns education. We need to reinforce our efforts to give the young generation the necessary skills and knowledge to use all these new technologies related to EO.
What are Earth Observation Data Cubes (EODC)? How does this technology revolutionize the access and use of satellite data?
Pressures on natural resources are increasing and several challenges need to be overcome to meet the needs of a growing population in a period of environmental variability. Some of these environmental issues can be monitored using remotely-sensed Earth Observations (EO) data that are increasingly available from a number of freely and openly accessible repositories. However, the full information potential of EO data has not been yet realized. They remain still underutilized mainly because of their complexity, increasing volume, and the lack of efficient processing capabilities.
EO Data Cubes (DC) are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatiotemporal data in an analysis-ready form.
The concept of the Data Cube is a series of structures and tools that calibrate and standardize datasets, enabling the application of time series and the rapid development of quantitative information products.
Before the Data Cube, satellite imagery and other gridded geospatial datasets were downloaded, analyzed and provided to users on a custom basis. It took a long time to produce an output and it came at a high cost, while only serving a single purpose. The “Data Cube” on the contrary, is a new way of organizing Earth Observations data by gathering all satellite images through space and time for a given period over a dedicated region. This is a change of paradigm in the way that remote sensing data are being organized before delivering it to end-users. The Data Cube (DC) approach calibrates the information to make it more accessible, easier to analyze, and to reduce the overall cost for users.
Do you foresee novel approaches for EO data acquisition, management, distribution, and analysis? Can you recognize a trend in this field?
There are many approaches that are influencing this field. I think that in terms of data management, distribution and analysis, the Data Cube technology is certainly gaining a lot of interest with many countries and even regions that are implementing Data Cubes and are facilitating the access and use to satellite EO data. Just have a look at Digital Earth Africa, DE Americas, DE Pacific or DE Australia. They are strongly influencing how users are accessing data and this is a trend that will continue to grow.
To inspire youth working with data cubes, what is a good starting point? What training material is there that practitioners should not miss?
If you go on the Open Data Cube website, you will find many useful resources to start with the Data Cube technology. In our team we also developed our own training material called « Bringing ODC into practices » helping users to install, index, and use satellite data in DC environment.
More broadly, Google with the Earth Engine or Microsoft with the Planetary Computer, are also contributing the facilitate the access and analysis of petabytes of satellite data all over the world.
What are the skills a user must have to explore the main functionalities of the open data cube?
I think that skills related to data science are essential. Python programming language is necessary, and obviously knowledge in remote sensing, time-series analysis, and machine learning are assets to fully exploit the content of DCs.
What are the main opportunities and challenges in the use of Earth Observation Data Cubes (EODCs) for environmental analysis and monitoring? How can we best use their potential? What needs to be done to overcome the challenges?
I think that EODC are creating a major opportunity to leverage the information power of satellite data. It allows, in the same infrastructure, to analyze both space and time in a given area. It helps to understand the past by looking at trends, determining the present situation, and possibly (when connecting with different models) exploring possible scenarios for the future.
Nevertheless, to be efficient an EODC should have sufficient processing power, this means that having access to cloud or HPC infrastructures is an essential pre-condition.
It is also essential to have access to in-situ data to train and validate models and ensure that the information produced is relevant, consistent, and corresponding to what can be observed on the ground. And this is probably currently one of the biggest challenges because in-situ data are not often openly shared.
What are currently the main applications of EODC? What is the potential use of EODC in the water sector?
There are many potential applications in the water sector ranging from water quality monitoring, surface water dynamics, floods detection, to impacts of climate change on water resources (such as rivers or vegetation affected by frequent droughts). For example, Australia has developed the Water Observations from Space algorithm to classify each pixel from Landsat satellite imagery as ‘wet’, ‘dry’ or ‘invalid’. Combining the classified pixels into summaries, covering a year, season, or all of time (since 1987) gives information on where water is usually, and where it is rare. It helps to (1) Understand where flooding may have occurred in the past ; (2) Assist with wetland analyses, water connectivity, and surface-ground water relationships ; (3) get insights into surface water changes per year for drought analysis and (4) Understand the differences in water availability between summer and winter. They also developed an algorithm that combines satellite data with tidal modeling to map the typical location of the Australian coastline at mean sea level for every year from 1988 to 2021. Resulting shorelines and detailed rates of change show how beaches, sandspits, river mouths, and tidal flats have grown and eroded over time. This allows one to explore how coastlines respond to drivers of change, including extreme weather events, sea level rise, and urban development; prioritize and evaluate impacts of local and regional coastal management decisions; and support research into how and why coastlines have changed over time.
In Africa, with DE Africa, they are monitoring many lakes and their water quality (see an example: link) While in Switzerland, we are currently developing new methods to monitor snow cover, which is an important form of water storage, and also a good indicator of climate change.
Can you say something about the interoperability and comparability of data cubes? Can you plug in several data cubes for comparative analysis?
Interoperability is a really important issue. We still need major efforts to ensure that all these DCs are interoperable. I think that the emergence of the Spatio-Temporal Asset Catalog (STAC) standard is a key enabler, and we see already many DCs that have implemented this standard. Obviously in the ODC framework, it is also possible to instantiate the suite of OGC web services (e.g, WMS, WCS) that may also greatly facilitate the interoperability among DCs. The new OGC APIs are also something we need to look at with interest. But one big challenge remains to plug in several data cubes for comparative analysis: it is the standardization of processing. Indeed, within EODC, you will never move the data to a given processing facility. Instead, we should ensure that a given processing algorithm can be shared and executed as close as possible to the data. Therefore, efforts like the OGC WPS, WCPS, and Processing APIs are important and should be implemented in DCs.
Are you aware of any water-related API’s that provide analysis and processing to water-body EO data stored in existing data cube infrastructure?
You should look to the Australia Water Observation from Space (WOfS).
What are the drawbacks in working with data cubes, and what are the benefits?
Currently, we are lacking «simple/easy-to-use » interfaces to help beginners (and non-experts) to dig into the DC environment. Nevertheless, if you have basic knowledge of Data Science tools such as Jupyter Notebooks, then amazing possibilities are offered to you, accessing data ready to be analyzed (no need to do any geometric and/or atmospheric corrections) and spend all your time exploring in space and time of almost 40 years of satellite data.
What still needs to be done to realize the full information potential of EO data?
Certainly a better integration of in-situ and remotely-sensed data to fully benefit from Machine Learning techniques. And then also a wider acceptance from the different stakeholders/research communities on the potential benefits of satellite EO data.
As a lecturer at the University of Geneva, where do you see youth’s greatest potential compared to the current water researchers and practitioners?
They are probably more open to new technologies and are more aware of open and reproducible practices in science. And I think that data science is really a major enabler within our research community that will definitely help the young generation of scientists and practitioners to have a strong positive impact on environmental protection.