Copernicus Land Research Articles

Twenty Years of Land Accounts in Europe

Land use and its change impact food security, carbon cycling, biodiversity, and, hence, the condition of ecosystems to mitigate and adapt to climate change, support economic prosperity, and human well-being. To support and guide policy actions between the economy and the environment, harmonized time series datasets, transparent methodologies, and easily interpretable statistics are needed. Therefore, monitoring of the function and condition of lands and their change, along with properly agreed methodologies and freely accessible data, are essential. The Copernicus Land Monitoring Service has produced over 20 years of Corine Land Cover datasets for 39 countries in Europe, which allows continental-wide harmonized and comparable monitoring and accounting of land cover and land use change at a high thematic resolution and in a long time series (2000–2018). With the upcoming 2024 update, the time series will reach a unique product worldwide in terms of time series length, spatial resolution, extent, and thematic detail, enabling policymakers and the scientific community to address the main anthropogenic drivers of land and ecosystem degradation. This paper describes a unified approach for producing continental-wide land accounts that aligns with internationally agreed-upon standards for measuring the environment and its relationship with the economy. Furthermore, the study provides a harmonized time series of geospatial data for deriving land accounts and provides statistics of land cover and land use status and changes for a twenty-year period. All geospatial data and statistics presented in this paper are freely accessible and downloadable to serve other studies.

Conceptual Model for Integrated Meso-Scale Fire Risk Assessment in the Coastal Catchments in Croatia

Various factors influence wildfire probability, including land use/land cover (LULC), fuel types, and their moisture content, meteorological conditions, and terrain characteristics. The Adriatic Sea coastal area in Croatia has a long record of devastating wildfires that have caused severe ecological and economic damages as well as the loss of human lives. Assessing the conditions favorable for wildfires and the possible damages are crucial in fire risk management. Adriatic settlements and ecosystems are highly vulnerable, especially during summer, when the pressure from tourist migration is the highest. However, available fire risk models designed to fit the macro-scale level of assessment cannot provide information detailed enough to meet the decision-making conditions at the local level. This paper describes a model designed to assess wildfire risks at the meso-scale, focusing on environmental and anthropogenic descriptors derived from moderate- to high-resolution remote sensing data (Sentinel-2), Copernicus Land Monitoring Service datasets, and other open sources. Risk indices were integrated using the multi-criteria decision analysis method, the analytic hierarchy process (AHP), in a GIS environment. The model was tested in three coastal catchments, each having recently experienced severe fire events. The approach successfully identified zones at risk and the level of risk, depending on the various environmental and anthropogenic conditions.

Analysis of the temporal and spatial evolution of turbidity in Tonle Sap Lake and its influencing factors

Turbidity is a crucial indicator of water quality. The European Commission's Copernicus Land Monitoring Service Platform provides free turbidity data for large lakes to monitor the global water quality of lakes. However, the data were missing from April 2012 to April 2016, severely limiting long-term analysis. Based on MODIS and turbidity data, Random Forest and XGBoost models are used to invert Tonle Sap Lake's turbidity. Random Forest outperformed the XGBoost model. Based on Random Forest model, missing data were filled in to construct long-term series data of Tonle Sap Lake turbidity (2004–2021). Trend, persistence and correlation analyses were conducted to reveal spatiotemporal characteristics and driving mechanism of turbidity. The results showed that: (1) spatially, the average annual, monthly, and seasonal turbidity was higher in the north but lower in the south, with regions of higher turbidity exhibiting more significant changes; (2) temporally, the annual turbidity mean was 53.99 NTU and showed an increasing trend. Monthly, turbidity values were higher from March to August and lower from September to February, with the highest and lowest recorded in June and November at 110.06 and 5.82 NTU, respectively. Seasonally, turbidity was higher in spring and summer compared to autumn and winter, with mean turbidity values of 84.16, 93.47, 15.33 and 23.21 NTU, respectively; (3) In terms of sustainability, the Hurst exponent for annual turbidity was 0.23, indicating a reverse trend in the near future; (4) Dam construction's impact on turbidity was not significant. Compared with natural factors (permanent wetlands, grasslands, lake surface water temperature, and remote sensing ecological index), human activities (barren, urban and built-up lands, croplands and population density) had a more significant impact on turbidity. Turbidity was highly correlated with croplands (r = 0.76), followed by population density (r = 0.71), and urban and built-up lands (r = 0.69).

Estimation of Spatiotemporal Changes of Land Cover in Llapi River Catchment (Northeastern Part of Kosova)

Aim of the studyIn recent decades, Kosova in general and the Llapi River catchment have experienced landscape transformation which resulted in Land Use and Land Cover (LULC) changes, mainly in flat terrains where most of country’s population is concentrated. Other landscapes have experienced changes too, but on a small scale. The aim of the study was to identify landscape transformation and driving forces behind it.Material and methodsSpatiotemporal changes of Land Use and Land Cover (LULC) were made using GIS techniques. Land Cover data from Copernicus Land Monitoring Service (CLMS) with spatial resolution 100 m for three different years: 2000, 2006 and 2018 were analyzed. DEM with 10 m spatial resolution was used to estimate slope steepness and catchments’ altitude. Population data for settlements inside the Llapi River catchment represented in features were downloaded from the Statistical Agency of Kosova, and those data include population numbers for official censuses between 1948 until 2011, while ArcMap 10.8 environment was used for analysis.Results and conclusionsWith GIS technique in analyzing Land Use and Land Cover data (LULC) and other open source data, we found that agricultural land have decreased in area by 9.53%, while the increase of artificial surface is 160.87%, or expressing by absolute values, total agricultural areas in 2000 were 37,827 ha, while in 2018 were 34,334 ha, meaning 0.3ha per capita, less than European level. In order to protect agricultural land, zoning and land use plans should be implemented.

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing — a case study in Germany

Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} by 2030 and to eliminate it by 2050. About 55 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} of soil are damaged (average 2018–2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} and 0.8 ha day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany’s sustainable development strategy.

The global atlas of edible insects: analysis of diversity and commonality contributing to food systems and sustainability

The future of the food system on the planet is increasingly facing uncertainties that are attributable to population growth and a surge in demand for nutritious food. Traditional agricultural practices are poised to place strain on production, as well as natural resources and ecosystem services provided, particularly under a changing climate. Given their remarkable attributes, including a low environmental footprint, high food conversion ratio, rapid growth and nutritional values, edible insects can play a vital role in the global food system. Nonetheless, substantial knowledge gaps persist regarding their diversity, global distribution, and shared characteristics across regions, potentially impeding effective scaling and access to edible insects. Therefore, we compiled and analysed the fragmented database on edible insects and identified potential drivers that elucidate insect consumption, globally, focusing on promoting a sustainable food system. We collated data from various sources, including the literature for a list of edible insect species, the Global Biodiversity Information Facility and iNaturalist for the geographical presence of edible insects, the Copernicus Land Service library for Global Land Cover, and FAOSTAT for population, income, and nutritional security parameters. Subsequently, we performed a series of analytics at the country, regional and continental levels. Our study identifies 2205 insect species, consumed across 128 countries globally. Among continents, Asia has the highest number of edible insects (932 species), followed by North America (mainly Mexico) and Africa. The countries with the highest consumption of insects are Mexico (450 species), Thailand (272 species), India (262 species), DRC (255 species), China (235 species), Brazil (140 species), Japan (123 species), and Cameroon (100 species). Our study also revealed some common and specific practices related to edible insect access and utilisation among countries and regions. Although insect consumption is often rooted in cultural practices, it exhibits correlations with land cover, the geographical presence of potentially edible insects, the size of a country’s population, and income levels. The practice of eating insects is linked to the culture of people in Africa, Asia, and Latin America, while increased consciousness and the need for food sustainability are driving most of the European countries to evaluate eating insects. Therefore, edible insects are becoming an increasingly significant part of the future of planetary food systems. Therefore, more proactive efforts are required to promote them for their effective contribution to achieving sustainable food production.

German and European Ground Motion Service: a Comparison

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn. As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.

Employing Copernicus Land Service and Sentinel-2 Satellite Mission Data to Assess the Spatial Dynamics and Distribution of the Extreme Forest Fires of 2023 in Greece

In 2023, Greece faced its worst wildfire season, with nine major fires causing unprecedented environmental damage of 1470.31 km2. This article uses Copernicus Land Monitoring Service and Sentinel-2 data, employing advanced remote sensing and GIS techniques to analyze spatial dynamics, map burn severity, assess fire extent, and highlight pre-fire tree density and land cover. The study focuses on the catastrophic fire in the Evros region and the damage to the National Forest Park of Dadia–Lefkimmi–Soufli. It also analyzes significant fires in Rhodes, Attica, Thessaly, Evia, Corfu, and Magnesia, emphasizing the compounded challenges posed by terrain, climate, and human factors in those areas. Additionally, the climate data for each affected area were compared with the weather conditions prevailing at the time of the fires. Copernicus Land Cover and Tree Density data are integrated to aid future management, assessment, and restoration. The analysis of maps and fire statistics underscores a notable pattern: areas with higher pre-fire tree density experienced correspondingly higher burn severity. This research underscores the crucial role of such data in assessing wildfire impact. In addition, compared with Copernicus Emergency Management Service, the burned area maps validate the accuracy and reliability of the utilized satellite data. The total burned area was assessed with a high accuracy rate of 96.28%.

Analyzing accessibility to urban services for new residential buildings. A case study of Craiova city

The present study uses methods based on Geographic Information System (GIS) to investigate the distribution of new residential buildings in Craiova (Romania) and assess their accessibility to key urban services. The focus is on collective housing developments constructed after 2015, encompassing both completed and ongoing projects. The research evaluates accessibility at the neighborhood level, considering key facilities and services such as banking services, pharmacies, kindergartens, primary schools, supermarkets, sport facilities, and green urban areas. Data for urban services were collected from opensource databases (OpenStreetMap, Copernicus Land Monitoring?Urban Atlas) and completed by field investigations. Isochrones, representing travel time from each residential building to the selected services, are used to measure the residents' accessibility by multiple travel modes. Additionally, the study considers the proximity of new residential buildings to the old historical city center, as a hub for shopping and leisure activities, and the walkability of the neighborhoods where the collective residences are located. The findings shed light on the spatial distribution of new residential developments in relation to essential urban services, providing valuable insights for urban planning and development strategies in Craiova, as well as for future residential investments.

Spatial planning needs towards Copernicus Land Monitoring Services: Case studies from Poland and Norway

The objective of this study is to identify the needs related to geospatial LC, LU, and LCLUC information for spatial planning in Poland and Norway, and examine the usefulness of CLMS products in the context of these planning systems. The research has conducted based on a comparative analysis of two planning systems, to indicate areas where CLMS can improve or supplement national spatial data. The study shows that CLMS can provide information on up-to-date spatial data showing actual LC/LU/LCLUC, but that the degree of detail and the accuracy may be insufficient. CLMS data is harmonised across Europe and thus meets the need expressed by international organisations, for data that are consistent at a continental level. This is not a requirement in national planning systems in Poland and Norway, where the needs are regulated by national legislation. The thematic and geometric accuracy of national data sources are usually better than the data provided by CLMS, but CLMS might fill gaps when specific topics are missing in national mapping programs.

Links between Land Cover and In-Water Optical Properties in Four Optically Contrasting Swedish Bays

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) in the respective catchment of each bay. The optical properties were measured in situ over the last decade by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8/OLI data. The absorption coefficient of colored dissolve organic matter (CDOM) at 440 nm, aCDOM (440), was significantly correlated to Wetlands which may act as sources of CDOM, while Developed areas (Agricultural and Urban classes) were negatively correlated. The Agriculture class was also negatively related to suspended particulate organic matter (SPOM), whilst Coniferous Forests and Mixed Forests as well as Meadows were positively correlated. SPOM seems thus to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land use and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale.

Assessing the spatial coherence of forest cover indicators from different data sources: A contribution to sustainable development reporting

The accuracy of forest area estimates has improved over time as a result of field/cadastral surveys, enhanced remote sensing techniques, and the effectiveness of algorithms for automatical recognition of land cover types. However, forest statistics seem to be less accurate in disaggregated spatial domains such as small administrative units. To evaluate the contribution of different data sources to small-area forest cover estimation in Europe, we compared seven indicators with different spatial coverage and resolution. The analysis considered multiple information sources from innovative initiatives, such as the Copernicus Land monitoring scheme, and traditional (national) surveys. More specifically, the study examined the spatial coherence of these indicators at the municipal scale in Italy to achieve two objectives: (i) assessing the overall precision of forest cover rates and (ii) identifying spatial variations in forest cover rates associated with the technical characteristics of each data source. A spatial econometric approach was used to identify the sources of spatial divergence in forest cover rates and determine the data providers best suited to meet the information requirements of environmental reporting at the desired spatial scale. The results reveal that the selected indicators show varying degrees of internal coherence, with some indices displaying strong correlations and others delineating heterogeneous spatial patterns. Our study highlights the importance of choosing the right information source assessing forest area at the municipal level and provides a valuable approach quantifying the coherence and reliability of environmental indicators in monitoring key aspects of sustainable development.

Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH4), remain challenging. We estimated LULUCF-related CH4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH4 emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH4. Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories.

ADVANCED ANALYSIS TOOLS FOR THE EUROPEAN GROUND MOTION SERVICE DATA

Abstract. This paper is focused on Advanced Differential Interferometric SAR (A-DInSAR). In the first part, the paper describes the European Ground Motion Service (EGMS), a new service of the Copernicus Land Monitoring Service. The EGMS provides consistent, regular, standardized, harmonized and reliable information regarding natural and anthropogenic ground motion over Europe. It is derived from A-DInSAR analysis of Copernicus Sentinel-1 data. The EGMS includes three main products: Basic, i.e. basic deformation maps delivered for individual and consistent frames of image stacks; Calibrated, i.e. deformation map integrated into a reference Global Navigation Satellite System network; and Ortho, which includes the East-West and Up-Down deformation, obtained by combining the ascending and descending results. The second part of the paper describes examples of data analysis. The first one is a clustering of PSI results, which highlights the so-called Active Deformation Areas (ADAs). The second one is an automatic classification of the ADAs. The third analysis concerns the differential deformation maps, which are useful to highlight areas where high deformation gradients occur. Such gradients are often associated to damages in buildings and infrastructures.

Accuracy of the Copernicus High-Resolution Layer Forest Type (HRL FTY) assessed with domestic NFI sampling plots in Poland

Abstract Over the past years, several remote sensing maps of land cover have been produced, but they still exhibit certain differences compared to the real land use that reduce their value for climate and carbon cycle modelling as well as for national estimates of forest carbon stocks and their change. This paper outlines a straightforward framework for evaluating map accuracy and estimating uncertainty in land cover area, specifically for forest-related land cover maps in Poland for the year 2018. The study compares stratified field-based data from the National Forest Inventory (NFI) with remote sensing data on forest variables, at the pixel level, in order to identify suitable methods for accuracy and area uncertainty estimation. Additionally, the paper introduces and presents a variety of accuracy metrics applicable to assess overall uncertainties in GHG inventories. The results indicate that the High-Resolution Layer Forest Type (HRL FTY) product (part of the broader Copernicus Land Monitoring Service [CLMS] portfolio), assessed using NFI field-based information, achieved an overall accuracy (OA) of 69.2%. This metric varies among particular nature protection forms, with the highest observed ones in Natura 2000 sites of 70.45%. The primary source of map errors was associated with distinguishing between broad-leaved and coniferous forest areas. Improving future maps necessitates more precise differentiation between species to better support national forest monitoring systems for the purpose of greenhouse gas (GHG) inventories where information on the spatial distribution and variability of forests sources, biodiversity assessment, threat prevention, estimation of carbon content is becoming an important part of the associated reporting system.

Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.

Monitoring and Mapping Urban Sprawl Over Heritage Hotspots Using Copernicus Land Monitoring Services: The case of periurban large-scale, wind-powered water extraction mills in Palma (Mallorca)

Among the varied group of human constructions of heritage interest are the old wind-powered water extraction mills that sometimes form large-scale sets in rural areas with high wind potential. On the Mediterranean island of Mallorca (Spain), up to 2,400 windmills dating from the 19th and early 20th centuries have been counted. The areas in which they are located are considered heritage ‘hotspots’, i.e., areas prone to specific problems, such as the progressive abandonment of ethnological heritage resulting from urban sprawl over areas with an agricultural orientation. This article aims to monitor urban sprawl in the municipality of Palma, to quantify and map its impact on a set of windmills located mainly in the plain of Sant Jordi, to the east of the city. The study has been carried out using methodologies and analysis techniques from the Copernicus Land Monitoring Service’s Urban Atlas and Imperviousness Density products. The study shows that areas with agricultural land uses have been progressively transformed into urbanised ones. This transformation has impacted, above all, windmills located in peri-urban areas adjacent to the city. The analysis aims to show the analytical possibilities of Copernicus services and products, and their applicability in the planning and management of peri-urban agro-industrial heritage.

Dealing with the uncertainty of technical changes in the CORINE Land Cover dataset: The Portuguese approach

Land Use and Land Cover (LULC) datasets are widely used across disciplines, with many users demanding more and better information. Understanding the uncertainties and errors associated to the main LULC datasets is a required step to facilitate their correct use, as well as to identify what could be improved in the future production of these products. CORINE Land Cover is probably the most well-known and used LULC dataset in Europe, especially valuable for the rich time-series that it provides. Despite being produced through a change mapping first approach, which tries to avoid technical errors and uncertainties in the temporal analysis of LULC changes, the Copernicus Land Monitoring Service distributes status layers of CORINE (CSL), which are not valid for change analysis because of their associated errors and uncertainties. The CORINE layers of changes (CHA) remove a lot of these issues, but do not meet the needs of many users. In Portugal, the national authority in charge of producing CORINE, the DGT, has implemented a backdating approach to produce consistent CSL layers that allow change analysis with low levels of uncertainty. Throughout this paper, we evaluate the changes that can be analyzed through all available CORINE layers in Portugal: Copernicus CSL layers; the national DGT CSL layers; and CHA layers. To this end, we aim to assess what type of changes can be studied through each type of layer, their associated sources of uncertainty and the relevance and utility of the Portuguese backdating approach to produce a consistent time-series of LULC maps. The results prove how the Portuguese CORINE layers distributed by the Copernicus Land Monitoring Service contain important sources of uncertainty, which however have been removed through the national backdating methodology. This methodology can be therefore exported for the production of CORINE in other European countries.

Unsupervised Machine Learning for GNSS Reflectometry Inland Water Body Detection

Inland water bodies, wetlands and their dynamics have a key role in a variety of scientific, economic, and social applications. They are significant in identifying climate change, water resource management, agricultural productivity, and the modeling of land–atmosphere exchange. Changes in the extent and position of water bodies are crucial to the ecosystems. Mapping water bodies at a global scale is a challenging task due to the global variety of terrains and water surface. However, the sensitivity of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) to different land surface properties offers the potential to detect and monitor inland water bodies. The extensive dataset available in the Cyclone Global Navigation Satellite System (CYGNSS), launched in December 2016, is used in our investigation. Although the main mission of CYGNSS was to measure the ocean wind speed in hurricanes and tropical cyclones, we show its capability of detecting and mapping inland water bodies. Both bistatic radar cross section (BRCS) and signal-to-noise ratio (SNR) can be used to detect, identify, and map the changes in the position and extent of inland waterbodies. We exploit the potential of unsupervised machine learning algorithms, more specifically the clustering methods, K-Means, Agglomerative, and Density-based Spatial Clustering of Applications with Noise (DBSCAN), for the detection of inland waterbodies. The results are evaluated based on the Copernicus land cover classification gridded maps, at 300 m spatial resolution. The outcomes demonstrate that CYGNSS data can identify and monitor inland waterbodies and their tributaries at high temporal resolution. K-Means has the highest Accuracy (93.5%) compared to the DBSCAN (90.3%) and Agglomerative (91.6%) methods. However, the DBSCAN method has the highest Recall (83.1%) as compared to Agglomerative (82.7%) and K-Means (79.2%). The current study offers valuable insights and analysis for further investigations in the field of GNSS-R and machine learning.

N°368 – Non-neurologic diseases mimicking stiff person syndrome

In climate modelling, a critical issue is the accurate representation of urban areas in space and variation over time. Urban features may be parameterised through urban canopy parameters (UCPs), including the impervious surface area (ISA). One of the aims of the Consortium for Small-scale Modelling (COSMO) and the CLM-Community is to improve urban numerical weather prediction (NWP) and climate simulations by implementing the urban canopy scheme TERRA_URB in the ICOsahedral Nonhydrostatic (ICON) modelling framework. To harmonise and update the datasets adopted for the UCPs, the Consortium has launched the Priority Project “City Induced Temperature change Through A'dvanced modelling” (PP CITTÀ). Within this framework, the study explores recent and detailed land cover datasets to update ISA and harmonise it with datasets adopted for the UCPs. We identify and compare some relevant datasets at the pan-European level. The results show the benefits obtained by correlating the UCPs from urban Local Climate Zones taken from the ECOCLIMAP-Second Generation with the ISA depending on the imperviousness degree provided by the Copernicus Land Monitoring Service. These results will foster an updating of NWP and climate models with urban surface parameterisations to match urban features while avoiding inconsistencies consistently.