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Small differences in the content of light, far more subtle than what we can perceive as different colours, carry valuable information about the health status of vegetation.

Hyperspectral imaging reveals this by splitting light into a large number of spectral components. Remote sensing applications use this spectral data to derive accurate information of even minor changes in the state of vegetation, water or other objects. With compact hyperspectral cameras, vegetation can be monitored from multiple platforms, including small drones and satellites. VITO Remote Sensing wants to make the extraction of accurate information at high update rates more accessible, to support precision agriculture, environmental and vegetation monitoring.

EXPLOITING THE POTENTIAL OF HYPERSPECTRAL IMAGING AND SMALL AIRCRAFT

Small drones have made high resolution local monitoring accessible. By equipping them with miniature hyperspectral imagers, their potential can be increased. But for that, new technologies are needed. A particularly well-suited technology is based on thin film interference filters (or linear variable filters – LVF) which can be deposited directly onto imaging sensors. This creates a very compact design which can be produced in a cost effective way. Fundamentally different from traditional hyperspectral imagers, such imagers allow to acquire 2-dimensional images by sensing different parts of the spectrum at different pixel locations. Developed by our partner imec, the Belgian research and innovation center in nano-electronics and digital technology, VITO Remote Sensing has been involved in optimising this technology for remote sensing requirements.

GET THE INFO OUT OF YOUR DATA

A compact sensor or camera is only part of the solution. Users also need to get the information out of the acquired data. By adding a user-friendly image processing flow you create a useful remote sensing tool. This is where VITO Remote Sensing steps in. Thanks to our knowledge and experience in image processing, image quality and remote sensing applications, we have created a unique hyperspectral imaging solution, COSI (compact hyperspectral imaging).

THE DARK HORSE AMONG MINIATURE HYPERSPECTRAL CAMERAS

Some other hyperspectral cameras have been developed based on thin film filters, but with filters arranged in conventional mosaic patterns similar to those in consumer colour cameras. Such imagers are stuck with a fundamental compromise in which only low spectral and spatial resolutions can be obtained. By changing to the LVF design, we can use photogrammetric methods and image processing to generate a hyperspectral data product with a wider spectral range and vastly improved spectral and spatial resolution. After the development of COSI, a prototype camera using a 2 Mpixel sensor (developed by imec), a second generation product was developed in collaboration with German camera builder Cubert gmbh . COSI became ButterflEYE LS and is available on the market since the end of 2016.

A GROWING FAMILY OF LVF BASED HYPERSPECTRAL IMAGING SOLUTIONS

VITO Remote Sensing has also been leading a Belgian consortium (VITO (prime), imec, Deltatec, AMOS, CMOSIS, under the authority of ESA) to develop a larger (12 MPixel) hyperspectral imager based on the same technology. The goal is to build an image sensor chip which acquires both hyperspectral and broad band panchromatic images that can be used on a wider range of remote sensing platforms, both airborne and spaceborne. A prototype camera will be available mid 2017.

An important milestone for thin film based hyperspectral systems is the in-orbit demonstration of the HyperScout instrument on board of a cubesat which will be launched mid 2017. This system is developed by Cosine in collaboration with VITO, S&T and TUDelft. To overcome limited downlink capacity (a very pressing limitation for hyperspectral imagers on small platforms) the system contains a powerful on-board processing module which converts the raw data into derived map products. Only these need to be transmitted to earth, which makes the system vastly more efficient.

Source

Land is an essential natural resource for humanity and all terrestrial ecosystems, but human demands exceed available resources. This leads to deforestation, drought, decrease of biodiversity and loss of wildlife. The Copernicus Global Land Service assists public and private organizations in the preservation of our ecosystems, where understanding is the first step.

COPERNICUS GLOBAL LAND COVER MAP

To tackle deforestation or loss of biodiversity for example, organizations first have to know the physical coverage of the Earth’s surface, its use and its dynamics. The Copernicus Global Land Services therefore extend its portfolio and released its first Global Land Cover Map to provide spatial information about land for a diversity of applications ranging from global forest monitoring, global crop monitoring, biodiversity and nature conservation to climate modelling.

By merging remote sensing imagery with other ancillary data sources, a highly automated, accurate and cost efficient Land Use and Land Cover (LULC) and Land Use and Cover Change (LUCC) solution generates yearly 100 m land cover maps, following the classification scheme of the FAO Land Cover Classification System (LCCS).

TUNE THE MAP TO YOUR APPLICATION

Next to this basic map, a set of continuous cover layers for tree, grass, shrub and bare soil are generated based on a novel approach. Each cover layer provides the fraction of the pixel that belongs to the given class. Through this information users can combine the layers and tune the default land cover classes for their application, and thus support the United Nations’ Sustainable Development Goals (SDGs).

Image of Tanzania showing the land cover classification on the left, a google earth image in the middle and the cover fraction compilation on the right.

THE DATA BEHIND THE MAP

The map is generated and validated by IIASA, Wageningen University and VITO Remote Sensing,. It is derived from PROBA-V 100 m and 300 m images and uses training data collected at 10 m resolution and several ancillary data layers.

The map and its cover layers were generated on the PROBA-V Mission Exploitation Platform, a scalable Hadoop Spark platform with 1200 executers and 5TB memory. Such platform is able to handle large amount of data and enables us to perform multiple iterations to perform quality checks and improve the products as it takes only a few days to perform all processing steps.

LAND COVER CLASSIFICATION FOR AFRICA

The first map covers the African continent in 2015. An independent validation shows accuracies up to 10% higher than other global land cover maps. A qualitative comparison shows significant improvements in terms of spatial accuracy compared to other global maps, especially in the Sahel zone, border regions of Sudan, Ethiopia and Kenya as well as eastern Botswana and majority of Madagascar. The map is assessed against both its generic use as well as against its use in different applications. The continuous cover fractions provide information at 100 m resolution with an accuracy of 85-95% (mean absolute error of 5-15%).

WHAT’S STILL TO COME?

The generation of the land cover maps is part of the Copernicus Service, hence sustained delivery of yearly updates. The next step, planned for mid-2018, is to scale up to the globe, deliver three yearly maps and integrate Sentinel-2 higher spatial details for selected regions.

More information can be found on the Copernicus Global Land portal. Users are encouraged to provide feedback through the Geo-WIKI validation tool or through the Copernicus Global Land helpdesk.

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This study proposes a synergistic landslide detection framework using Sentinel 1 and Sentinel 2 data and applying several change detection algorithms.

As case study, the landslide of the Amyntaio’s mine in Greece at 10/6/2017 was selected. The landslide was rapidly and effectively detected through operational and automatic processes. Thus, rapid and reliable conclusions can be extracted for decision making and risk monitoring.

Impact

Operational, rapid and automatic landslide detection provides vital information for decision making and risk monitoring. Copernicus/ESA data can contribute to this topic as provide free use, efficient information, high temporal frequency, and big area cover. Multimodal approaches such a synergy of Sentinel 1 and 2 processes can lead to proper detection of landslide phenomena.

Concept

The main goal of this research is the rapid and automatic detection of large landslide phenomena using Sentinel 1 and 2 data applying several change detection algorithms.

Technical Details

Several change detection techniques were used to map the large landslide of the Amynteou mine. The used time series data from Copernicus program, i.e before the landslide and after the landslide, were: 2 SAR images type IW/SLC from Sentinel 1 (4/6/2017 and 10/6/2017) and 2 EO images type MSIL2A from Sentinel 2 (1/6/2017 and 21/6/2017). Concerning the Sentinel 1 data, change detection maps were extracted using the magnitude and the phase layer. On the other hand, concerning the Sentinel 2 data, change detection maps were extracted through direct image processing and machine learning using spectral information and annotated data. The landslide detection was performed via change detection techniques at ERDAS IMAGINE software 2016.1 (Imagine SAR Feature tool, Imagine SAR Interferometry tool, Imagine Objective tool, and Change Detection tools).


Used time series data from Sentinel 1 and 2


Change detection strategy and results using Sentinel 1 and Sentinel 2 data

Contact Info
o Contact person : Maltezos Evangelos, Betty Charalampopoulou
o E-mail : mail@geosystems-hellas.gr

Extreme weather events are causing significant shifts in the productivity of agricultural activities posing a danger to food security and yield production. Improving weather forecasts is essential when it comes to dealing with the effects of climate change and save agricultural investments.

In response to these threats, the UK Space Agency and a consortium of UK companies led by RHEA Group, are working in partnership with the Ugandan Ministry of Water and Environment and the Ugandan National Meteorological Association (UNMA) to effectively develop and implement a solution that can anticipate and proactively respond to these weather irregularities.

The Drought and Flood Mitigation Service (DFMS) uses localised weather forecasts, combined with the latest satellite and ground-based data and the derivation of improved drought and flood models. The application delivers high-quality, timely, geo-information to its users, enabling them to efficiently respond to both, possible negative and positive, effects of forecasts on agriculture and livestock production.

The Technology Behind DFMS

DFMS uses the Environment Early Warning Platform (EWP) to communicate with local farmers on actions to be taken throughout the growing season to maximise crop yields and protect their livestock. The EWP will assimilate a range of diverse data sources, ranging from satellite and meteorological data to community/mobile sources that are obtained in the field. The system will use cloud technologies for flexible deployment and processing

EWP includes data from seasonal forecasts, linked hydrological modelling for drought and flood, satellite imagery from Copernicus and other missions, soil moisture, land surface temperature, water level extents, radar images, and land cover classification. The accumulation of this data will be displayed on a secure, reliable, platform, accessible via an open data cube.

The region: Karamoja, Uganda

DFMS initially will work in the Karamoja region of Uganda, an area with high levels of poverty and vulnerability, where 10% of the total population lives (more than one million people). Their farmers currently receive weather forecast information via the radio, and they also use their indigenous methods to predict rainfall. Unlike the current weather prognostications methods, DFMS provides parish level of detail, ensuring more specific and accurate data.

Currently, the effects of climate change impact an estimate of 80% of the Ugandan population. Irregular weather patterns, such as the timing of the onset of the rainy season and the reliability and intensity of precipitation, are increasing crop failure, soil erosion, and land degradation disrupting both small-holder livelihoods as well as agricultural businesses.

Drought and Flood Mitigation Service
info@dfms.co.uk
www.dfms.co.uk

Dr Sam Lavender from Pixalytics Ltd was a speaker at ‘One Step Beyond’ a TEDx Leicester event at the National Space Centre in Leicester, UK which took place on the 21st September. Sam gave a talk entitled “Beyond The Blue Ocean” which demonstrated through satellite imagery the different aspects of the ocean that can be seen from space.

The imagery was displayed within the Space Centre’s planetarium and so the screen was huge!

TEDx are independent events within the global TED organisation dedicated to spreading ideas and sparking conversations through short talks. Sam’s video will join the TEDx library (https://www.youtube.com/user/TEDxTalks/videos ) in the near future.

AgriMonitor is a web app, that allows the monitoring of agricultural areas through time either by using interactive graphs of vegetation and soil indices, or image snapshots that describe the intra-field crop growth conditions at various spatial and time resolutions

NEUROPUBLIC is an active member of the European Association of Remote Sensing Companies, that develops cloud EO-based services for the agricultural sector. We are doing business by using Copernicus and IoT data, promoting the penetration of EO deeper into the CAP line of business and also the establishment of the digital farming market (figure 1).

AgriMonitor is a web app that allows the monitoring of agricultural areas through time, either by using interactive graphs of vegetation and soil indices, or image snapshots that describe the intra-field crop growth conditions at various spatial and time resolutions. The information context is enriched by processed data coming from our own network of telemetric stations.

For most areas of the Greek Territory, agronomists and farmers are now able to understand at parcel level the evolution of their crops. It’s a time machine that enables decisions under a straightforward way (figure 2).

The end user may upload his agricultural parcels or he can digitize an Area of Interest (AOI) and see the results in real time. The AOI polygons may also represent large areas, such as wetlands, grasslands or even lakes and forests. This way, Agrimonitor can arise as a valuable way to monitor the evolutionary change of whole ecosystems through time.

Methodologically, at NEUROPUBLIC we have adapted an object-based approach, aiming to extract with an automated way objective and well-documented information. Each agricultural parcel, is an object under monitoring. We assign extracted indicator values to agricultural parcels, having as main input streams of Earth Observation-based layers, ground-sensor measurements, or even farm logs.

The assigned info creates a multi-dimensional signature of the object that describes the parcel under different aspects.  We present the assigned info by using graphs or images. We can also use these datasets to create models that allow the extraction of insights, or the classification using various criteria. The services as well as the processing workflows have been created using open source software. We mainly use Python in the backend as there are great packages available to handle spatial data along with zonal operations and arrays. Considering the maps and the charts presented in UI, we focused on interactive libraries, such as Leaflet and Plotly. The existing infrastructure of Agrimonitor has already been tested in the development of Smart Farming and CAP services. Our future steps include the integration of Sentinel 1 and 3 data as well as the extension of the coverage at a EU level. NEUROPUBLIC will demonstrate Agrimonitor at the upcoming 23rd MARS Conference that will take place in Gormanston, Ireland, on 28 and 29 November 2017.

Gisat participates in the SEN4CAP project focusing at implementation & demonstration of operational applications & products to support the management of the CAP.

The SEN4CAP project aims at providing to the European and national stakeholders of the CAP validated algorithms, products, workflows and best practices for agriculture monitoring relevant for the management of the CAP. Special attention is given to provide the evidence how Sentinel derived information can support the modernization and simplification of the CAP in the post 2020 timeframe. Initial results will be already available in early 2018 to support informing the policy and guidelines development of the future CAP.

The main SEN4CAP objectives are:

  • Identify and specify EO products and services suitable to increase the efficiency, traceability as well as reducing the costs of the IACS;
  • Develop algorithms along with open source code for agricultural EO products based on Sentinel-1 & -2 responding to the user requirements;
  • Demonstrate and validate the developed agricultural EO products up to national scale;
  • Provide evidence for the utility of Sentinel products within IACS procedures at EU and national level for 6 national Paying Agencies;
  • Prepare and facilitate the transfer of developed EO products and services to the national Paying Agencies including capacity building and demonstrating cloud computing capabilities.

The SEN4CAP project will further rely on the guidance of a steering committee composed of the EC actors of the CAP, namely representatives from DG-Agri (Unit D.3), DG-JRC (Food Security unit) and DG-Grow (Copernicus). The steering committee will provide advice on the evolving CAP policy and legal framework, the technical guidelines of the IACS implementation and the Copernicus context.

Gisat is responsible for the implementation of Agricultural Practices Monitoring use cases. They are mostly focused to improve the efficiency and traceability of the control of the greening measures of the CAP, in particular dedicating 5% of arable land to ‘ecological focus areas’. Three practices are proposed as separate use cases: check of compliance with declared land lying fallow; catch crops and nitrogen fixing crops. Additional use cases will develop methods to identify crop harvest on arable land parcels and ploughed grassland parcels.

SEN4CAP is funded by the European Space Agency under the EOEP-5 programme. The project begun in July 2017 and will run for 2.5 years, until December 2019. The project consortium is composed of 5 organizations: UCLouvain-Geomatics (project lead, Belgium), CS Romania, e-GEOS (Italy), Gisat (Czechia) and Sinergise (Slovenia).

Gisat provides wide range of geoinformation services based on Earth Observation technology. It focuses on operational application of satellite mapping to monitor various aspects of our environment and development of dedicated web based platforms for geoinformation analysis and assessment
Tel: + 420 271741935 Fax: + 420 271741936
E-mail
Web

Gisat participates in the CLIMATE-FIT project aiming at implementation & demonstration of urban climate services in Europe.

Urban areas are very vulnerable to climate change impacts, because of the high concentration of people, infrastructure, and economic activity, but also because cities tend to exacerbate climate extremes such as heat waves and flash floods. The objective of the CLIMATE-FIT project is to establish a service that translates the best available scientific urban climate data into relevant information for public and private end-users operating in cities.

This will be achieved by demonstrating the benefits of urban climate information to relevant end user communities, considering service components in the following domains: Climate and Health, Building Energy, Emergency Planning, Urban Planning, Active Mobility, and Cultural Heritage.

During the first phase of the 30-month project, end users (which are included as partners) and climate service providers will be involved in the co-design/-development of six concrete sectoral cases and in the demonstration to relevant end user communities. Each of these cases will be subject to a detailed socio-economic impact analysis, quantifying the benefits of using urban climate information. The second phase of the project will focus on upscaling and market replication, initially aiming at the extension with six new cases, involving new (non-financed) end-users. Through a business development strategy, supported by dissemination and marketing activities, the project aims at demonstrating the long-term market viability of the service.

In the longer run, CLIMATE-FIT aims at a genuine market uptake of urban climate services, based on a distributed network of local business intermediaries throughout Europe, enhancing the awareness for urban climate-related issues in the end-user community, and converting (mature) research results into tailored added-value information, thus removing important barriers for the deployment of urban climate services.

Gisat is responsible for the implementation of Urban Planning service. The development will be done in close cooperation with cities of Prague, Ostrava and Hodonin and it will target to cover wide range of their information needs related to climate change:

  • Assessment of the city’s potential for further development with respect to the threats of urban climate change;
  • Monitoring and prediction of the urban microclimate (action versus non-action impacts);
  • Impact assessment of densification of urban development versus the resilience to urban climate change;
  • Assessment of existing urban greenfields to mitigate urban heat waves;
  • Modelling of city development and the associated urban cooling capacity towards various climate change scenarios.

The service implementation will also include the development of web based tool to enable dedicated tailored scenario analysis based on climate change modelling and varying urban land use datasets, and representing different city development strategies.

CLIMATE-FIT / PUCS is funded by the European Union Horizon 2020 Research and Innovation Programme, SC5-01-2016-2017: Exploiting the added value of climate services. The project begun in June 2017 and will run for 2.5 years, until November 2019. The project consortium is led by VITO (Belgium) and it is composed of 14 organizations.

Gisat provides wide range of geoinformation services based on Earth Observation technology. It focuses on operational application of satellite mapping to monitor various aspects of our environment and development of dedicated web based platforms for geoinformation analysis and assessment
Tel: + 420 271741935 Fax: + 420 271741936
E-mail
Web

EUROSENSE was awarded by the Brussels Capital Region with the contract to update the map of the “capital of Europe”

Based on 5cm airborne data, the photogrammetric update of the map is realised for one third of the Region per year. This will result in a complete and up-to-date 3D database of Brussels in 2019.


Image of the Berlaymont building (20 April 2017- 5cm GSD)

August 2017 – Increasingly, the advantage of combined spatial and temporal analysis is being recognized, and very much so currently in agroinformatics. Rasdaman is enabling novel agroinformatics services on many levels, and international conferences seek talks on rasdaman and its open-standard approach.

EOFarm is a hitech startup in Greece with a mission in supporting farmers with Big Data Analytics. The open-source rasdaman community platform has enabled a quick deployment of the service while ensuring its scalability and openness. Products offered include Color Composites, Band Ratios and Indices, Vegetation Detection, Canopy Greenness Estimation, Land Surface Temperature, Time series over arbitrary areas of interest, etc. With Landsat8, Sentinels, and !RapidEye the underlying data sets integrate both open and commercial satellite imagery. Due to the success of this service, meanwhile a similar framework has been deployed for water quality monitoring.

In Germany, Spatial Business Integration GmbH and rasdaman GmbH team up in the BigPicture project which focuses on “diagnosis in the field”: Big-Data-based determination of causes for satellite image derived and site-specific variations so that targeted measures can be recommended to farmers, such as fertilizer placement, application of plant protection products, choice of species to grow, etc. To achieve high reliability, 500 farmers are delivering ins-situ insight for ground truthing. Further, meteorological data are mixed in. Again, rasdaman is the enabling Big Datacube engine underneath. The project is being supported by the German Federal Ministry of Food and Agriculture.

Meantime, the expertise of rasdaman CEO Peter Baumann in Big Datacubes is being sought continuously. In Kampala / Uganda this August he was invited keynote speaker at the Advances in Geomatics Research Conference where a central topic was how to best use Geo IT for an effective, yet environmentally compatible agriculture. Right after, he presented at the annual Agro-Geoinformatics Symposium at George Mason University in Fairfax, USA. At the INSPIRE conference in Kehl he will give a tutorial on INSPIRE WCS: from Mystery to Mastery, followed by talks at ESA EO Open Science Conference iN Frascati, Italy, and at INTERGEO Berlin, among others.

Contact: Dr. Peter Baumann, baumann@rasdaman.com