A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python

The ERATOSTHENES Centre of Excellence (ECoE) as a digital innovation hub for Earth observation

The "EXCELSIOR" H2020 Widespread Teaming Phase 2 Project: ERATOSTHENES: EXcellence Research Centre for Earth SurveiLlance and Space-Based MonItoring Of the EnviRonment is supported from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 857510 for a 7 year project period to establish a Centre of Excellence in Cyprus. As well, the Government of the Republic of Cyprus is providing additional resources to support the establishment of the ERATOSTHENES Centre of Excellence (ECoE) in Cyprus. The ECoE seeks to fill the gap by assisting in the spaceborne Earth Observation activities in the Eastern Mediterranean and become a regional key player in the Earth Observation (EO) sector. There are distinct needs and opportunities that motivate the establishment of an Earth Observation Centre of Excellence in Cyprus, which are primarily related to the geostrategic location of the European Union member state of Cyprus to examine complex scientific problems and address user needs in the Eastern Mediterranean, Middle East and Northern Africa (EMMENA), as well as South-East Europe. An important objective of the ECoE is to be a Digital Innovation Hub and a Research Excellence Centre for EO in the EMMENA region, which will establish an ecosystem where state-of-the-art sensing technology, cutting-edge research, targeted education services, and entrepreneurship come together. It is based on the paradigm of Open Innovation 2.0 (OI2.0), which is founded on the Quadruple Helix Model, where Government, Industry, Academia and Society work together to drive change by taking full advantage of the cross-fertilization of ideas. The ECoE as a Digital Innovation Hub (DIH) adopts a two-axis model, where the vertical axis consists of three Thematic Clusters for sustained excellence in research of the ECoE in the domains of Atmosphere and Climate, Resilient Societies and Big Earth Data Management, while the horizontal axis is built around four functional areas, namely: Infrastructure, Research, Education, and Entrepreneurship. The ECoE will focus on five application areas, which include Climate Change Monitoring, Water Resource Management, Disaster Risk Reduction, Access to Energy and Big EO Data Analytics. This structure is expected to leverage the existing regional capacities and advance the excellence by creating new programs and research, thereby establishing the ECoE as a worldclass centre capable of enabling innovation and research competence in Earth Observation, actively participating in Europe, the EMMENA region and the global Earth Observation arena. The partners of the EXCELSIOR consortium include the Cyprus University of Technology as the Coordinator, the German Aerospace Center (DLR), the Leibniz Institute for Tropospheric Research (TROPOS), the National Observatory of Athens (NOA) and the Department of Electronic Communications, Deputy Ministry of Research, Innovation and Digital Policy

Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe

The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe

The current landscape of disaster resilience education in Europe

Focus on disasters resilience among the EU countrie