Earth Observation for Early Warning of Land Degradation at European Frontier

Project Information

EWALD

Grant agreement ID: 101086250

DOI

10.3030/101086250

EC signature date 21 September 2022

Start date 1 December 2022

End date 30 November 2026

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 1 416 800,00

Coordinated by

COFAC COOPERATIVA DE FORMACAO E ANIMACAO CULTURAL CRL
Portugal

Periodic Reporting for period 1 - EWALD (Earth Observation for Early Warning of Land Degradation at European Frontier)

Reporting period: 2022-12-01 to 2024-11-30

Summary of the context and overall objectives of the project

Land degradation (LD) is the world's greatest environmental challenge affecting the environment, agriculture, and human wellbeing. Intensified by natural disasters and desertification, the LD may present potential risks and socioeconomic tension at the European Union (EU) frontier.
The project aims to develop an innovative framework to provide an Early Warning System (EWS) and responses to the LD threatening the EU from its external border, using multi-source and multi-scale Earth Observation (EO) data.
This framework will be a remarkable economic, innovative, and reliable approach allowing an in-depth and objective evaluation of LD using remote sensing (RS) over the extensive territories of the countries with different socio-economic backgrounds. We use the examples of Ukraine and Morocco as the test regions (TRs). It will be a core engine for the future decision-making system aimed to prepare communities threatened by LD to act promptly and appropriately to reduce the possibility of harm or loss. The complete technology for LD early warning using multisource and multiscale EO data will be elaborated. It will include:
(1) EWS Prototype Design,Elaboration of the EWS conceptual framework. Establishment of a set of LD indicators & models describing LD processes at multiple scales. Formulation of the requirements on multisource & multiscale EO-based products applied in the EWS Prototype. Formulation of the requirements on hazards and vulnerability assessment.
(2) EWS Prototype Development, Advanced Remote Sensing (RS) technical platform development. Elaboration of the workflows for big Data processing and Machine learning aiming to reveal trends of LD indicators from EO-based data. Elaboration of the workflows for LD risks forecasting from EObased LD indicators. Conversion of the elaborated workflows to the appropriate warning tool realized in cloud computing, in particular, working from isolated sites
(3) EWS Prototype Validation, Validation and demonstration of the application-oriented EWS for at-site LD processes assessment over large areas.

The EWS Prototype will be elaborated to provide early warning and responses to LD threatening of the territories at the EU external frontier. The innovative complex approach to integrating the multisource and multiscale EO data, novel remote sensing, and modeling techniques with risk knowledge, the EWS Prototype will provide monitoring and forecasting of the LD processes to make the right decisions.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

WP1 (TECHNOLOGIES) – ongoing. Start/End Month 1/48
According to the planned Task 1.1 EWS Prototype Design, a conceptual framework of the Early Warning System, formulating requirements for the actions of its further development, has been elaborated. Opening activities have been undertaken to point out existing general concepts on LD risk assessment and early warning. Core activities have been executed to elaborate a conceptual framework on LD risk assessment and early warning. The deliverable proposes a suitable architecture of a cloud-based EWS for LD risk assessment using satellite EO and other necessary geospatial data. (D.1.1 - submitted).
T1.1 Leader: CASRE. Support: ECOMM, Lusofona, UCAM, RESING.
T1.1 Duration: Start/End Month 1/12
Under the Task 1.2 EWS Prototype Development, the testing of the elaborated concept has been started. The preliminary results are available via the website: https://ewald-hub-ewald.hub.arcgis.com/ and ArcGIS Online: https://ewald.maps.arcgis.com/. For this purpose ArcGIS Pro, a full-featured professional desktop GIS application from Esri, has been purchased and deployed on high-performance computing facilities in UNIZA. Elaboration of the workflows for big Data processing and Machine learning aiming to reveal trends of LD indicators from EO-based data have also been started.
T1.2 Leader: UNIZA. Support: CASRE, ECOMM, Lusofona.
T1.2 Duration: Start/End Month 12/32
WP2 (CAPACITY BUILDING) – ongoing. Start/End Month 1/48
Activities on Task 2.1 Staff training, including personnel core training to mastering the conception, basic techniques and modern algorithms for big data distributed processing and high-skill staff training in RS methods, in big-data processing of multi-temporal time series of satellite images, has been fulfilled during:
1. International Workshop on Earth Observation for Early Warning of Land Degradation at European Frontier (EWALD), June 20th - 22nd, 2023, Zilina, Slovakia (Leader: UNIZA. Support: CASRE, ECOMM, Lusofona, UCAM, RESING)
2. Conference on the role of space technology and satellite imagery in land degradation assessment and landslide monitoring, October 4, 2023, Marrakesh Morocco (Leader: UCAM. Support: CASRE)
3. Workshop on Earth Observation for Land Degradation Early Warning Workshop for ORMVAO, October 10th, 2023 Ouarzazate, Morocco (Leader: UCAM. Support: CASRE)
4. Dissemination Workshop on Earth Observation for Early Warning of Land Degradation at European Frontier, November 16, 2023, Žilina, Slovakia (Leader: UNIZA. Support: CASRE)
Online courses “Unmanned Aerial Vehicles: Planning and Operation” and “Introduction to Google Earth Engine” has been developed by CASRE and will be provided to UCAM and ORMVAO (Office Régional de Mise en Valeur Agricole), as a key stakeholder for Moroccan test region, at the beginning of 2024.
Main achievement is a staff trained in RS & Big Data processing.
T2.1 Leader: UNIZA. Support: UCAM, CASRE, ECOMM, Lusofona, RESING.
T2.1 Duration: Start/End Month 1/14
WP3 (STRATEGIES) - ongoing. Start/End Month 1/48
Task 3.1 Recommendations on enhancing responses to land degradation at the local and regional scale within the EU external frontier are in progress. The review of the current understanding of LD threats early warning is carried out using EU-standardized EO-based methods and tools, including the analysis of the key indicators of land condition for Sustainable Development Goals and targets of the 2030 Agenda for Sustainable Development, international conventions (UNCCD, IPCC, IPBC) and UN & EU Programmes.
Main achievement as a manual for application of remote sensing techniques to assess, monitor and identify land degradation at early stages (D3.1) will be developed by M19 (June 2024).
T3.1 Leader: CASRE. Support: UCAM, RESING, Lusofona
T2.1 Duration: Start/End Month 1/14

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

As a result of intersectoral secondments, partners from UCAM/RESING after the regular training in the best-quality European structures will jump to the qualitatively new level in RS technologies and land management. This training will continue after the end of the Projects, using, e.g. such tools as Erasmus+; Finally, the academic organization of the project, bypassing the administrative complications, will get sustainable links to the regional organizations, which also require their output. The benefit will be bilateral.
We strongly believe that the created network, having the outstanding potential, capacity and multisectorial structuring, extended from the satellite image acquisition and processing and large-scale information processing to the recommendation elaboration and their regional implementation will grow during the lifetime of the Project to the competitive European-scale Consortium, able to solve the regional LD problems and not only by RS methods.