Taiwan is usually one of the rainiest places on the planet, however for the past year, little rain has fallen, consequently resulting in the worst drought on the island for the last 56 years. Many water reservoirs throughout the country are falling below 20 % capacity while some are less than 10 % and in danger of drying up.

The detrimental impacts of the water crisis in Taiwan are felt throughout the country and water reduction policies have been implemented in many different sectors and industries to save limited water resources. The water crisis could also have global repercussions by affecting Taiwan’s $100bn semiconductor industry which is responsible for manufacture of up to 90% of worlds advanced microchips.

In a country where water resources have historically been plentiful, the impacts of a changing climate and unsustainable water resources management practices could have dire consequences. The first step towards sustainable water resource management is to ensure proper and timely information on water (non-) availability to inform water management activities.

Through our ESA funded project 'WorldWater', novel approaches to monitor surface water dynamics in both extent and volume, are being developed, to demonstrate the potential of earth observation to underpin cost-effective and robust monitoring of water resources at all scales. Applying these new methods and tools, we uncovered the water dynamics in both extent of volume over the past two years, in Taiwan’s largest reservoir ‘Tsengwen’, which as of April 2021 reportedly is at less than 12 % of its capacity.

Through an integrated approach, using both Sentinel 1 (SAR), 2 (optical) and 3 (altimetry) data, the entire water regime of Tsengwen reservoir can effectively be monitored in high temporal and spatial resolution from space. As can be seen from the illustration below, as of late March 2021, the reservoir is more than 10 m below its median level from 2018-2021, and the low water level is also clearly depicted as the difference between the surface water dynamics between 2019-2020 and 2020-2021.

Offshore wind has been growing rapidly in recent years, as an attractive and clean energy alternative. However rigorous environmental legislation and a harsh and dynamic environment require thorough and meticulous planning, as well as continuous operational monitoring of windmill installations and the dynamics of the surrounding water bodies, to ensure safe and reliable operation of offshore wind assets.

The majority of the data and information needed to plan, construct and operate offshore wind farms is costly, time consuming and risky to collect, requiring rigorous field campaigns and expensive equipment.

Satellite-based products and services provide a powerful, low cost and efficient alternative to supplement or replace traditional monitoring solutions, by delivering up-to-date and comprehensive intelligence on the dynamics of the seabed and conditions of the environment around offshore wind installations.

Our satellite based services for offshore wind farms include;

• Very High resolution satellite imagery to identify features such as wind turbine direction, vessel details etc.
• Satellite-derived bathymetry for cost-efficient mapping of shallow water depth.
• Mapping of seabed mobility of large sand waves.
• Mapping of aquatic vegetation.
• Coastal mapping for cable landfall and environmental studies.
• Suspended sediment monitoring.
Stay tuned for more updates on Offshore wind – we will share concrete use cases and examples of application over the next months.

Topographic maps of Greenland are generally not up to date and the positional accuracy is often inadequate in the breathtaking landscapes outside the main settlement areas. This means that decisions related to public administration, environmental regulation and the potential for economic development are often based on data and maps that may be inaccurate, and this can seriously impede decisions based on these data.

This situation is about to change very soon though, as the Danish government (Through the Agency for Data Supply and Efficiency (SDFE)) has asked for help from space.

In close collaboration with SDFE and our partners in Airbus Defence and Space , we have processed vast amounts of updated SPOT 6 and 7 satellite imagery covering 225.000 sq.km of Greenland, in a very short time. We have turned these data into cloud free, orthorectified and color optimized image mosaics in 5 different parts of Greenland. The results are fresh views of the astonishing landscape of Greenland.

Stay tuned for updates on getting access to the imagery, which will be freely available very soon

Earth observation infrastructure is developing rapidly these years, with increasingly more satellites being send into space, some of these in large swarms of entire microsatellite constellations. Consequently, satellite remote sensing is not limited by a lack of data, but rather our capacity to make best use of all the data we have available already. The key is to build autonomous and intelligent systems on top of satellite imagery data streams, with a view to detect, analyse and process images faster and more accurately than ever before.

Imagine the powerful combination of a living digital library that documents every inch of our planet on a daily basis, and the tools and platforms allowing us to extract meaningful insight from this library at scale. We increasingly apply deep learning-based approaches to serve this purpose, allowing us to build effective and scalable solutions that serves unique insight about the change and dynamics on our planet. We use deep learning to underpin the information needs and requirements within many different sectors, including:

• Maritime industry for detection of reefs, hazardous objects, submerged aquatic vegetation, ships and illegal vessels, icebergs and dynamics in ports and harbours.
• Construction industry and urban spatial planning for detection of roads, impervious surfaces, roof top types, cars, parking spaces, solar energy potential and general activity within construction zones.
• Wind energy for monitoring metocean conditions around offshore wind energy sites, mapping wind breaks in open landscapes and estimating surface roughness conditions (including forest height and density)

For more information about the power of deep learning and how we apply it in DHI GRAS, explore the links below