GeoVille (Austria)

Changing climatic circumstances bring more frequent and intense extreme weather events that significantly impact forests in various ways. Since forests are the largest terrestrial sinks for carbon, and are among the richest biological environments on Earth, the goals of understanding the related challenges and improving the forest resilience is high on the agenda to mitigate climate change and save biodiversity. Achieving these goals requires access to data to derive vitality and health of trees, monitor and forecast tree growth, environmental conditions data, as well as suitable data modelling approaches.Within our research, we exploited a wide set of data sources originating and ranging from remote sensing to in-situ measurement equipment allowing us to address the tree growth and health from different spatial and temporal points of view.The data from dendrometers provided us with the high frequency (hourly), intraday variation of tree radial growth for assessing long-term growth and instantaneous changes in growth. These data are of extreme value, as no other means to monitor trees on such a high temporal resolution with a very high sensitivity exits. However, to understand the variations in these data, which directly show variation in the tree growth, especially in the context of extreme or sudden changes, they are evaluated within the environmental context. The environmental high-quality data were collected directly from forest sites selected from the Europe-wide Forest monitoring program (ICP-Forests), which has been providing high-quality data on the vitality and adaptability of trees, nutrient cycles, water balance, etc.  Furthermore, satellite Earth Observation (EO) data for single-tree detection and monitoring forest disturbances like selective logging and drought impacts have been likewise exploited, to explore if they may have an impact on the individual tree growth. We show that a CNN-based U-Net model trained on Very High Resolution (VHR) imagery demonstrates strong potential for identifying tree crowns and validating changes in forest structure. However, challenges such as limited training data diversity and low resolution for small trees underscore the need for further refinements.Finally, terrestrial laser scanning (TLS) technique delivers single tree point-clouds not only allowing extraction of traditional tree features like diameters at different heights, tree height and crown dimensions, but also providing the possibility of statistical approaches for calculation of various metrics, e.g., point-cloud percentiles along the tree height and tree competition.We describe the approaches on leveraging these, the challenges we have encountered (e.g., data gaps, errors in data, co-location), how we approached them,  and all in the context of developing predictive AI-based, climate sensitive tree growth models, to support forest management on a local, regional and national level, and thus empowering response to minimize potentially harmful consequences for modern societies in line with the UN Sustainable Development Goals.

Dilijan National Park is one of the most important national parks of Armenia, established in 2002 to protect its rich biodiversity of flora and fauna and to prevent illegal logging. The aim of this study is to provide first, a mapping of forest degradation and deforestation, and second, of land cover/land use changes every 5 years over a 28-year monitoring cycle from 1991 to 2019, using Sentinel-2 and Landsat time series and Machine Learning methods. Very High Spatial Resolution imagery was used for calibration and validation purposes of forest density modelling and related changes. Correlation coefficient R2 between forest density map and reference values ranges from 0.70 for the earliest epoch to 0.90 for the latest one. Land cover/land use classification yield good results with most classes showing high users’ and producers’ accuracies above 80%. Although forest degradation and deforestation which initiated about 30 years ago was restrained thanks to protection measures, anthropogenic pressure remains a threat with the increase in settlements, tourism, or agriculture. This case study can be used as a decision-support tool for the Armenian Government for sustainable forest management and policies and serve as a model for a future nationwide forest monitoring system.

A number of glaciological observations on debris-covered glaciers around the globe have shown a delayed length and mass adjustment in relation to climate variability, a behavior normally attributed to the ice insulation effect of thick debris layers. Dynamic interactions between debris cover, geometry and surface topography of debris-covered glaciers can nevertheless govern glacier velocities and mass changes over time, with many glaciers exhibiting high thinning rates in spite of thick debris cover. Such interactions are progressively being incorporated into glacier evolution research. In this paper we reconstruct changes in debris-covered area, surface velocities and surface features of three glaciers in the Patagonian Andes over the 1958–2020 period, based on satellite and aerial imagery and Digital Elevation Models. Our results show that debris cover has increased from 40 ± 0.6 to 50 ± 6.7% of the total glacier area since 1958, whilst glacier slope has slightly decreased. The gently sloping tongues have allowed surface flow velocities to remain relatively low (<60 m a−1) for the last two decades, preventing evacuation of surface debris, and contributing to the formation and rise of the ice cliff zone upper boundary. In addition, mapping of end of summer snowline altitudes for the last two decades suggests an increase in the Equilibrium Line Altitudes, which promotes earlier melt out of englacial debris and further increases debris-covered ice area. The strongly negative mass budget of the three investigated glaciers throughout the study period, together with the increases in debris cover extent and ice cliff zones up-glacier, and the low velocities, shows a strong linkage between debris cover, mass balance evolution, surface velocities and topography. Interestingly, the presence of thicker debris layers on the lowermost portions of the glaciers has not lowered thinning rates in these ice areas, indicating that the mass budget is mainly driven by climate variability and calving processes, to which the influence of enhanced thinning at ice cliff location can be added.

<p>Agriculture provides essential social benefits: supply of food and commodities, economic development and employment. However, agriculture is under growing pressure, arising from soil degradation, water scarcity, natural hazards and weather extremes due to changes in climate patterns. Agricultural insurance is gaining an increasing role as a risk management tool. Given this, the insurance sector has a significant emphasis on identifying, gathering and aggregating historical and current regional and localised data, which could be sourced from remote sensing and earth observation (EO) datasets.</p><p>To find out more about the needs and challenges of the agro-insurance’s sector and how these might be addressed with current and future EO capabilities, the ESA Earth Observation Best Practice for Agro-Insurance (EO4I) project brings together the EO and agro-insurance sector. The latter is represented by a champion user group comprised of primary insurers as well as reinsurers. The very close and regular contact with those champion users is an outstanding characteristic of this project.</p><p>An analysis of the potential customers’ requirements revealed a list of more than 60 challenges and needs of the sector, such as the assistance in damage assessments, identification of potential risk effects, estimation of affected area and the extent of damage, or monitoring the crop development throughout the season. These challenges were translated into geo-information requirements for a better analysis of currently available EO capabilities. As could be seen so far, business processes of insurance industry can be supported by numerous remote sensing products and services.</p><p>Nevertheless, there is a major gap between the perceived potential and the actual application of available EO capabilities by the agro-insurance sector. The bottleneck is the lack of awareness, understanding and trust in the EO products and services for the agro-insurance sector on both sides, the insurers and their customers. The remote sensing community also often focusses on the possibilities and appropriateness of certain techniques, without considering the impact on the customer value, the productivity and profitability of the industry.</p><p>Therefore, EO methods, products and services need to be adaptable to the agro-insurance’s business needs and fit into their daily workflows. The project now builds on the results of this initial requirements analysis to connect EO products with insurance solutions to go from best practice to practice. To demonstrate the potential of EO and cutting-edge technology for the agricultural insurance sector, customised use cases to support loss assessment and monitoring based on artificial intelligence will be developed for selected areas and tested with the available in-situ data.</p>

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.

It is often difficult to retrieve timely and reliable information on agricultural production in areas under conflict. Security conditions disrupt the normal data collection and agricultural statistics are often not available, or of questionable accuracy. Satellite Earth observation (EO) is a powerful solution, as it can assess agricultural production in areas difficult to access and provide unbiased information on a wide range of spatial and temporal scales. The objective of this study was to analyse changes in cultivated area and agricultural productivity in Syria prior to and during conflict. As EO provides both historical and near-real time operational data, changes can be rapidly identified in a cost-effective, consistent and repeatable manner. Overall, we found that agricultural productivity in irrigated land in Syria has decreased since the start of the conflict. The results provided input to an integrated multi-sector analysis carried out by the World Bank to inform future policy decisions, reconstruction efforts and shape future development response. Analysis of status, changes, and trends in agricultural area extent and productivity helped to identify the need and most suitable types of intervention, the timing and the targeting criteria. Combining the results with other datasets allows the identification of drivers and aggregators in agriculture, particularly in the context of conflict and economy. Furthermore, the results could also be used to focus rehabilitation actions after the conflict ends, for example the planning of re-establishing agricultural supply chains.

The successful implementation of initiatives for water resource management and sustainable development requires access to reliable data and information on water-related issues. There is a growing awareness that Earth Observation (EO) data has the potential to serve these data needs especially in the context of International Financing Institutions (IFIs) and Official Developing Assistance (ODA) normally operating in regions where policies and management decisions are often based on sparse and unreliable information. This paper will provide examples on how EO is supporting development projects in Africa in order to help countries better measure and manage their water resources and as support for the monitoring requirements of the Sustainable Development Goals (SDGs). Through practical examples, it will be demonstrated how the new generation of Sentinel satellites offering free data access can provide large area monitoring information on surface water resources (including wetlands), and agricultural land use and practices with an unprecedented spatial detail and revisit time.

Intelligence delivered by earth observation (EO) satellites performs a vital role in supporting ICT enabled urban governance, and the creation of decision making tools delivering integrated urban planning. This paper reviews the DECUMANUS project experience, detailing the development of the EO derived tools, and evaluating the service products that facilitate the deployment of top-down expertise in land use planning. The central purpose of the paper is to assess the potential for use of these DECUMANUS high resolution EO images and data, also to support bottom-up participatory planning, promoting co-design. It is concluded: 1) EO derived images and associated data offer great opportunity to deliver top-down decision making tools, which combined with auxiliary data, including participatory sensing data, effectively support integrated urban planning; 2) EO derived images also offer substantial potential as communication tools, enabling citizens to make more informed and responsible choices and participate in co-designed urban planning.

Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT) that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP) satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm), its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in applications. To provide a comprehensive overview of themajor characteristics and caveats of the ASCATsoil moisture product, this paper describes the ASCAT instrument and the soil moisture processor and near-real-time distribution service implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).A review of themost recent validation studies shows that the quality of ASCAT soil moisture product is – with the exception of arid environments –comparable to, and over some regions (e.g. Europe) even better than currently available soil moisture data derived from passive microwave sensors. Further, a review of applications studies shows that the use of the ASCAT soil moisture product is particularly advanced in the fields of numerical weather prediction and hydrologic modelling. But also in other application areas such as yield monitoring, epidemiologic modelling, or societal risks assessment some first progress can be noted. Considering the generally positive evaluation results, it is expected that the ASCAT soil moisture product will increasingly be used by a growing number of rather diverse land applications.