Martyna A. Stelmaszczuk-Górska

The Earth Observation Training, Education, and Capacity Development Network (EOTEC DevNet) is a global network of networks in the forefront of integrating satellite Earth information into decision-making, especially in managing disasters. The Network focuses on fostering expert collaboration and knowledge sharing on the use of Earth Observations (EO) in improving disaster risk reduction efforts globally. The primary goal is to enhance the accessibility of EO tools and training. The network aims to support a broad audience, ranging from local authorities to international agencies, in effectively utilising EO data for disaster management. The approach involves aligning existing EO solutions with the needs of those managing hazards such as floods and droughts. Additionally, the network seeks to bridge the gap by disseminating knowledge to partner institutions and the entities responsible for implementation, aiming to harness the strengths of both and address the requirements of disaster risk reduction. Crucial to this initiative are the ‘Communities of Practice’ that form the backbone of EOTEC DevNet. These dynamic groups are the main drivers in the development of vital resources like the Flood Tools Tracker and the Drought Tools Matrix. These comprehensive guides assist users in selecting and utilising appropriate tools for varied disaster scenarios, showcasing commitment to enhancing stakeholder engagement with EO data across all disaster management stages. Beyond creating tools, this network of experts encourages learning through collaboration. Real-world cases of regional flooding and other disasters are analysed to show how EO tools can be used in practice. These studies/analysis highlights the impact of EO data in enhancing early warning systems and in the response and recovery from disasters. Lessons learned can be replicated elsewhere in the world as part of contribution to Disaster Risk Reduction. EOTEC DevNet fosters an interactive online community where experts can share knowledge and resources. This platform is a hub for connecting people based on their areas of interest in EO and disaster risk reduction. It plays a key role in our efforts to build a stronger network of professionals to join in driving and delivering on the UN-WMO global assignment of “Early Warning for All (EW4All)”. While also enhancing global capacity in disaster management. In summary, EOTEC DevNet is committed to improving disaster risk management through EO data. Our focus on collaboration, resource sharing, and practical application of EO tools is paving the way for more effective disaster management worldwide. The paper will present the operational structure of EOTEC DevNet; Establishment of; Meeting/Engagement Platform; a brief look at the Flood Tools Tracker and the Drought Tools Matrix; and other achievements that have been accomplished so far.

The EO4GEO Body of Knowledge (BoK) serves as a vocabulary for the domain of geoinformation and earth observation, supporting the annotation of online resources. This paper presents how the BoK is designed, maintained and improved. We discuss how the BoK content can be extended, using the example of integrating artificial intelligence (AI) concepts and show how annotation is done by adding persistent concept identifiers in the metadata of training materials. This platform allows us to share online information with clarified semantics. A prolonged use necessitates the incentivisation of an active expert community and a further adoption of infrastructure standards.

The EO4GEO Body of Knowledge (BoK) forms a structure of concepts and relationships between them, describing the domain of Earth Observation and Geo-Information (EO/GI). Each concept carries a short description, a list of key literature references and a set of associated skills which are used for job profiling and curriculum building. As the EO/GI domain is evolving continuously, the BoK needs regular updates with new concepts embodying new trends, and deprecating concepts which are not relevant anymore. This paper presents the inclusion of BoK concepts related to Artificial Intelligence. This broad field of knowledge has links to several applications in EO/GI. Its connection to concepts, already existing in the BoK, needs special attention. To perform a clean and structural integration of the cross-cutting domain of AI, first a separate cluster of AI concepts was created, which was then merged with the existing BoK. The paper provides examples of this integration with specific concepts and examples of training resources in which AI-related concepts are used. Although the presented structure already provides a good starting point, the positioning of AI within the EO/GI-focussed BoK needs to be further enhanced with the help of expert calls as part of the BoK update cycle.

The COVID-19 pandemic has a high impact on education at many different levels. In this study, the focus is set on the impact of digital teaching and learning at universities in the field of Earth observation during the COVID-19 pandemic situation. In particular, the use of different digital elements and interaction forms for specific course types is investigated, and their acceptance by both lecturers and students is evaluated. Based on two distinct student and lecturer surveys, the use of specific digital elements and interaction forms is suggested for the different course types, e.g., academic courses could be either performed asynchronously using screencast or synchronously using web meetings, whereas practical tutorials should be performed synchronously with active participation of the students facilitated via web meeting, in order to better assess the student’s progress and difficulties. Additionally, we discuss how further digital elements, such as quizzes, live pools, and chat functions, could be integrated in future hybrid educational designs, mixing face-to-face and online education in order to foster interaction and enhance the educational experience.

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.

The field of Earth Observation (EO) and Geoinformation (GI) is gaining more and more importance due to the increasing number of data and data processing algorithms to respond even more accurately to a variety of challenges in many application areas. In order to follow recent activities and align the exponential evolution of datasets and recent processing trends with market and academic training requirements, the EO*GI sector needs an updated and new definition of knowledge and skills. To this goal, a specific body of knowledge for Earth Observation and Geoinformation (EO4GEO BoK) is currently being implemented with the aim of providing interconnected concepts and job-oriented skills. One novelty of the BoK is to include topics related to Earth Observation, in particular to introduce the different sensors used in this field. Active optical sensors are at the crossroad between Earth Observation and close-range photogrammetry and have not been described in any other existing BoK. This paper introduces therefore the part of the EO4GEO BoK that is dedicated to active optical sensors. Such systems are used in various job-oriented applications such as archeology, mobile mapping, indoor or outdoor, or for surveying purposes, just to name a few. The structure of this part of the BoK is explained and specific descriptions and relationships between the identified concepts are given. Finally, the skills acquired by completing these BoK concepts are presented and discussed in terms of how they can be used in a professional context or in the definition of job-oriented learning paths.

With new Earth Observation (EO) and Geoinformation (GI) data sources increasingly becoming available, evermore new skills for data collection, processing, analysis and application are required. They are needed not only from scientists, but also from practitioners working in businesses, public and private EO*GI and related sectors. Aligning the continuously evolving skill sets demanded by the market and existing academic and vocational training programmes is not an easy task. Training programmes should be grounded in real needs of the sector and its labour market. To do this, it is necessary to identify the knowledge and skills needed, and map their interconnectivity in specific frameworks, which can later be used for the definition of new curricula or job-oriented learning paths. This paper presents a framework for the EO*GI sector, based on a Body of Knowledge (BoK), by creating a complete set of concepts with a semantic structure underneath that supports academia and industry. Creating and updating the BoK is supported by an editing tool, the Living Textbook and by experts in the EO*GI domain, who contributes to the BoK’s enrichment.

The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. Particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSE_cor) of 29.4 t ha⁻¹ was calculated. A marginal decrease in RMSE_cor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha⁻¹). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha⁻¹ to 4.7 t ha⁻¹; and a RMSE_cor of 30.2 t ha⁻¹ was calculated.

The main objective of this paper is to investigate the effectiveness of two recently popular non-parametric models for aboveground biomass (AGB) retrieval from Synthetic Aperture Radar (SAR) L-band backscatter intensity and coherence images. An area in Siberian boreal forests was selected for this study. The results demonstrated that relatively high estimation accuracy can be obtained at a spatial resolution of 50 m using the MaxEnt and the Random Forests machine learning algorithms. Overall, the AGB estimation errors were similar for both tested models (approximately 35 t∙ha⁻¹). The retrieval accuracy slightly increased, by approximately 1%, when the filtered backscatter intensity was used. Random Forests underestimated the AGB values, whereas MaxEnt overestimated the AGB values.