THE ESA LEARNEO! PROJECT FOR STIMULATING EARTH OBSERVATION EDUCATION

Fabio Del Frate (1), Pierre-Philippe Mathieu (2), Valborg Byfield (3), Chris Banks (3), Malcolm Dobson (4),

Matteo Picchiani (5), Vinca Rosmorduc (6)

(1): Tor Vergata University, Civil Engineering and Computer Science Engineering Department, Via del Politecnico 1, 00133, Rome, Italy

(2) European Space Agency (ESA-ESRIN), Via Galileo Galilei, 00044 Frascati, Italy (3) National Oceanography Centre (NOC), European Way, Southampton, SO14 3ZH, United Kingdom

(4) Bilko Develoment Limited (BDL), Dundee, United Kingdom (5) GEO-K s.r.l., Via del Politecnico, 1 - 00133 Rome, Italy

(6) Collecte Localisation Satellites (CLS), 31520 Ramonville St-Agne, France

ABSTRACT

LeanEO! is a 2-year Earth Observation education project funded by the European Space Agency (ESA) and developed by different European Institutions. Its main aim is to increase the understanding and knowledge of satellite data obtained from ESA missions and demonstrate how these can be used when faced with environmental problems in the real world. The project has developed hands-on training resources for use primarily (but not exclusively) by teachers and students at upper high school to university level. Each lesson comes complete with data, analysis tools and exhaustive background information necessary for the completion of the suggested activities and provides answers to the various study questions. Model answers are supplied for users working on their own or with limited specialist support. In this paper the aims and the opportunities provided by the project will be described in detail.

Index Terms— Earth Observation, Education, ESA

1. INTRODUCTION For society to benefit fully from its investment in Earth Observation, EO data must be accessible and familiar to a global community of users who have the skills, knowledge and understanding to use the observations appropriately in their work. To achieve this considerable education effort is required. In this paper we describe how the LearnEO! Project , a new ESA education initiative, contributes towards making this a reality. LearnEO! has two main objectives: to develop new training resources that use data from ESA satellites to explore a variety of environmental topics; and to stimulate and support members of the EO and education communities who may be willing to develop and

share new education resources in the future. The project builds on the UNESCO Bilko project, which currently supplies free software, tutorials, and example data to users in 175 countries. Most of these users are in education or research, but these training resources will also prove interesting to a growing number of professionals in government, NGOs and private enterprises. Typical users are not remote sensing experts, but see satellite data as one of many observational tools. They are looking for an easy, low-cost means to process, display and analyse data from different satellite sensors as part of their work in environmental research, monitoring and policy development. Many of the software improvements and training materials developed in LearnEO! are in response to requests from this user community. The LearnEO! tutorial and peer-reviewed lessons are designed to teach satellite data processing and analysis skills at different levels, from beginner to advanced - where advanced lessons require some previous experience with Earth Observation techniques. The materials are aimed at students and professionals in various branches of Earth sciences who have not yet specialized in specific EO technologies. The lessons are suitable for self-study, university courses at undergraduate to MSc level, or for continued professional development training. Complete data, analysis tools and background information are provided with each lesson. This will enable its users to complete the activities and answer the questions put forward. Furthermore, model answers are provided to aid users working on their own or with limited specialist support. The web site also provides access to more than 100 annotated data sets and a lesson developer’s resource library, both designed to support users who wish to develop their own lessons and tutorials and share these with others. Registered users are encouraged to become involved with the project by providing support for future software and lesson development, testing, and peer review.

1978-1-4799-1114-1/13/$31.00 ©2013 IEEE IGARSS 2013

2. THE LEARNEO! LESSONS The selected LearnEO! lessons will contribute to the training resources required for developing capacity to use data from ESA missions in several application areas. Complying with the needs of non-EO professional users of EO data, the lessons emphasize application to environmental research and on monitoring rather than detailed technical information with regard to the processing of data obtained from particular sensors. However, they do provide sufficient technical background to enable students to assess the strengths and limitations of the different data sets in respect to the application area covered by the lesson. Although the lessons are based on the use of data provided by ESA's EnviSat, GOCE, CryoSat and SMOS missions, they also use data from other sensors where appropriate. This gives a more complete picture and allows students to appreciate how synergy between different sensors and missions can provide more reliable, thorough and detailed information as opposed to data obtained exclusively from one single sensor. Different items will be provided for each lesson: Lesson overview introducing the topic and giving

detailed learning objectives and lists of data sets and software tools required to complete the lesson. This will be available both on the web site, and at the beginning of the PDF version of the lesson itself.

Complete lesson divided into chapters, each covering a specific learning objective, and providing brief explanations of the activities to be carried out, as well as detailed instructions for these followed by questions to aid the students comprehension of the subject.

Model answers to each question explaining the reasoning behind the answer, and if necessary referring the students to relevant background information.

Background information on technical and/or environmental topics that is needed to ensure students are able to fully understand the application area and that sufficient information is provided to be able to complete activities, interpret the data and answer the questions posed during the lesson.

References and further reading will list additional learning resources on-line, and refer to scientific papers accessed to support the information provided in the lesson. Wherever possible references will be linked to the paper on-line, or if copyright allows, a copy of the paper will be included in the lesson’s resources.

5.1. Topics The selection of the topics was made in order to show the significant support that EO could provide in different application domains concerning the environment and life on our planet. They regard the observation of land changes and urban sprawl, of forests and of the effects of fire, air quality analysis, monitoring of ice and of particular atmospheric

phenomena (such as El Niño). Gravity and geodesy issues have also been considered. At the current stage of the project, the following10 lessons have already been developed: o The Amazon River plume: One of the most obvious

features when looking at results from SMOS is the input of freshwater from the River Amazon. The Amazon is also responsible for delivering a vast quantity of nutrients into the Atlantic. This lesson uses these two features to compare the spatial scales over which changes in salinity and ocean colour can be viewed when using EO data.

o Monitoring oil pollution at sea: The lesson demonstrates how to use ASAR and MERIS data from the Gulf of Mexico oil spill in spring 2011 to retrieve information regarding the presence of oil on the sea’s surface.

o El Niño – Southern Oscillation (ENSO): This lesson shows the progression of an El Niño across the Pacific Ocean and its turn back to La Niña, using the power of the global view made possible by satellite observation.

o Monitoring Atlantic storms: The lesson introduces wind and wave measurements from satellites and shows how high ocean waves can become during a storm in the Atlantic (comparable to hurricane wave heights). It emphasizes the existence of along-track (non-imagery) data sets, and explains how they are acquired and how to interpret them.

o Observing Earth gravity: This lesson introduces the geoid as computed from GOCE measurements. It highlights some of the geoid features that match either deep inhomogeneities, or reliefs, and explains how these influence sea surface heights.

o Monitoring Arctic Sea ice: the Arctic is one of the areas most affected by global climate change but, at the same time, is also an area that is quite difficult to measure in situ. This lesson shows the different parameters available over ice from different EO sensors. The use of CryoSat data and the freeboard provided within is shown.

o Forest monitoring using SAR data: The potential of multi-temporal spaceborne SAR data for monitoring fire scars and subsequent vegetation recovery is illustrated in this lesson. It considers the specific case of a dramatic fire that occurred in a Mediterranean site. Synergy with optical data is also considered.

o Urban growth monitoring using SAR: The goal is to show how the identification of a set of SAR suitable features (for example temporal or textural features) is important for the setting-up of effective procedures for urban growth monitoring.

o Land cover mapping: The methods of analysis and exploitation of multi-spectral (MERIS) and hyperspectral (AHS) data for land cover mapping are illustrated. Additional information exploitable with

2

multi-angular acquisitions (CHRIS) is considered. Image classification is carried out and a methodology for the validation of the results is performed.

o Soil moisture monitoring: In this lesson the physical principles of soil moisture retrieval from SMOS and the processing chain necessary to produce soil moisture maps are shown. This lesson can be considered as a good example of the complexity of a remote sensing retrieval problem.

3. THE DATA SET The data that have been selected for the project have been evaluated as suitable for the corresponding target audience and its level of expertise. For beginners this has been achieved by choosing spectacular and exciting images that illustrate phenomena of interest even outside the scientific community. For users with increasing levels of expertise, the need for spectacular data is reduced, whereas it is still necessary to provide clear-cut examples of different phenomena, with patterns that are easy to discern given the right guidance. For more advanced users (e.g. professionals and post graduates), already familiar with a given application area, we believe it could also be appropriate to provide examples that exemplify its limitations as well as its benefits. Particular care was taken to ensure that different geographical areas of the globe were covered. If on one side this is not difficult to obtain for coarse spatial resolution data, particular attention was required in the selection of higher resolution data.

At least 100 data sets are provided within the project. The data include both Level 1 and higher level products from the range of ESA missions and sensors. Wherever necessary for the lessons, non-ESA images such as LANDSAT or airborne hyperspectral data (AHS) have also been considered. Multi-mission geophysical products that incorporate measurements from ESA sensors are included. The data also include analysis data produced by assimilating EO and/or in situ data into models. Finally, ground-truth data available stemming from ESA campaigns have been considered. All candidate images in the Data Sets are preliminarily checked to ascertain if they have any problems in terms of analysis and visualization by using Bilko software. It has to be noted that additional data have also been selected to guarantee that, besides those shown to the students during the lessons, enough complementary data will be available for additional independent practice.

In Fig. 1 a view of the geographical distribution of the data, as it appears on the LearnEO! website, is illustrated, while in Fig. 2 optical and SAR data provided in the context of the lesson about oil pollution monitoring are shown.

4. OTHER ON-LINE RESOURCES The web site open area (http://www.learn-eo.org/) consists of a homepage, and a hierarchy of web pages allowing users to browse LearnEO resources and select which ones to download (Fig. 3). Besides the option regarding the data (shown in Fig. 1), other areas will be accessible from the top menu bar, and are openly available to all website visitors: Author area offering advice, support and a forum for

registered lesson authors. Overview of lessons, with links to more information

and lesson downloads Software and tools descriptions, with links to

downloads and tutorials for image processing and display.

Searchable Resource Library with background information and extra tools

Fig. 1: Access to the data through the LearnEO website

Fig. 2: example of data available within the oil spill monitoring lesson: Envisat ASAR data (left), Envisat MERIS data (right)

3

Fig. 3: The LearnEO! Web-site (http://www.learn-eo.org/)

4