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

Do People Understand and Observe the Effects of Climate Crisis on Forests ? The Case Study of Cyprus

Recent reports stress the vulnerability of forest ecosystems in the European Union (EU), especially in the south. Cyprus is an island in the south of EU and the eastern of the Mediterranean Sea. While Cyprus’ vulnerability is stressed, Cyprus was included in the worst-performing countries regarding EU carbon emission’s targets of 2020. For mitigating climate change, Cyprus could benefit for tailored education and improved policy making. This study analyses the perceptions of the Cypriot residents about climate change and forest degradation aiming (1) to gain a better understanding of whether Cypriot residents understand its importance, (2) to understand if the general public is able to observe the changes noted in the literature, (3) to understand how perceptions are differentiated across different demographic categories, and (4) to derive correlations between demographic data and perceptions. This is a quantitative study; a questionnaire was used as a tool and the responses received were 416. It was highlighted that 65.62% of the participants stated that they noticed moderate to very much degradation of Cypriot coniferous forests. A potential degradation reason was written down by 150 people, of whom 31.33% referred to tree die-back, while many stated decreased soil moisture and difficulty in regeneration. All these reasons of degradation were either stated or suspected in the literature. Additionally, the demographic analysis showed that there may be an association between employability and beliefs/observations about climate change. The results of the research could be used for tailored education, further research, and promoting environmentally friendly policies. This will support Cyprus and other countries in reaching their Green Deal targets and, consequently, mitigate the severe effects of climate change

A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

In this paper, we investigate the performance of six data structures for managing {voxelised} full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.The Centre for Digital Entertainment, United Kingdom Plymouth Marine Laboratory, United Kingdo

Analysis of radar and thermal satellite data time-series for understanding the long-term impact of land surface temperature changes on forests

Forests are globally an important environmental and ecological resource since they retrain water through their routes and therefore limit flooding events and soil erosion from moderate rainfall. They also act as carbon sinks, provide food, clean water and natural habitat for humans and other species, including threatened ones. Recent reports stressed the vulnerability of EU forest ecosystem to climate change impacts (EEA, 2012) (IPPC, et al., 2014). Climate change is a significant factor in the increasing forest fires and tree species being unable to adapt to the severity and frequency of drought during the summer period. Consequently, the possibility of increased insect pests and tree diseases is high as trees have been weakened by the extreme weather conditions. In Cyprus, there are two types of pine trees that exists on Troodos mountains, Pinus Nigra and Pinus Brutia, that may have been influenced by the reduced snowfall and extended summer droughts during the last decades. The overarching aim of this project is to research the impact of Land Surface Temperature on Cypriot forests on Troodos mountains by analysing time-series of radar and thermal satellite data. Impacts may include forest decline that does not relate to fire events, decreased forest density and alternations to timing of forest blooming initiation, duration and termination. Radar systems emitted pulses that can penetrate forest canopy due to the size of its wavelength and, therefore, collect information between tree branches without being affected by clouds. This presentation will focus on radar analysis conducted; testing of various methods, and how the processing pipeline has been automated

Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

This article belongs to the Section Forest Inventory, Quantitative Methods and Remote SensingIn southern Australia, many native mammals and birds rely on hollows for sheltering, while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse Woody Debris—CWD) is a very different task from a classification perspective. This study focuses on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees have irregular shapes making delineation of them challenging. Additionally, since the study area is a native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods that will be resistant to those challenges. Previous study showed that detection of dead standing trees without tree delineation is possible. This was achieved by using single size 3D-windows to extract structural features from voxelised full-waveform LiDAR and characterise dead (positive samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the single 3D-windows approach and the new multi-scale 3D-windows approach were implemented for comparison purposes. The accuracy of the results was calculated using the precision and recall parameters and it was proven that the multi-scale 3D-windows approach performs better than the single size 3D-windows approach. This open ups possibilities for applying the proposed approach on other native forest related applications.This study is part of the “FOREST” Project (OPPORTUNITY/0916/0005), which is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation of Cyprus

A Selection of Experiments for Understanding the Strengths of Time Series SAR Data Analysis for Finding the Drivers Causing Phenological Changes in Paphos Forest, Cyprus

Observing phenological changes are important for evaluating the natural regeneration process of forests, especially in Mediterranean areas where the regeneration of coniferous forests depends on seeds and the changes in blossoming time are influenced by climate change. The high temporal resolution of Sentinel-1 data allows the time series analysis of synthetic aperture radar (SAR) data, but it is still unknown how these data could be utilised for better understanding forest phenology and climate-related alternations. This study investigates the phenological cycle of Paphos forest, Cyprus using SAR data from 1992 to 2021, acquired by ERS-1/2, Envisat and Sentinel-1. An average phenological diagram was created for each space mission and a more detailed analysis was performed from October 2014 to November 2021, using the higher temporal resolution of Sentinel-1 data. Meteorological data were used to better understand the drivers of blooming alternations. Using the interquartile range (IQR), outliers were detected and replaced using the Kalman filter imputation. Forecasting trend lines were used to estimate the amplitude of the summer peaks and the annual mean. The observation of the average phenology from each satellite mission showed that there were two main blooming peaks each year: the winter and the summer peak. We argue that the winter peak relates to increased foliage, water content and/or increased soil moisture. The winter peak was followed by a fall in February reaching the lower point around March, due to the act of pine processionary (Thaumetopoea pityocampa). The summer peak should relate to the annual regeneration of needles and the drop of the old ones. A delay in the summer peak—in August 2018—was associated with increased high temperatures in May 2018. Simultaneously, the appearance of one peak instead of two in the (Formula presented.) time series during the period November 2014–October 2015 may be linked to a reduced act of the pine processionary associated with low November temperatures. Furthermore, there was an outlier in February 2016 with very low backscattering coefficients and it was associated with a drought year. Finally, predicting the amplitude of July 2020 returned high relevant Root Mean Square Error (rRMSE). Seven years of time series data are limiting for predicting using trend lines and many parameters need to be taken into consideration, including the increased rainfall between November 2018 and March 2020

Reconstruction of a 3D Polygon Representation from full-waveform LiDAR data

This study focuses on enhancing the visualisation of FW LiDAR data. The intensity profile of each full-waveform pulse is accumulated into a voxel array, building up a fully-3D representation of the returned intensities. The 3D representation is then polygonised using functional representation (FRep) of geometric objects. In addition to using the higher resolution FW data, the voxels can accumulate evidence from multiple pulses, which confers greater noise resistance. Moreover, this approach opens up possibilities of vertical observation of data, while the pulses are emitted in different angles. Multi-resolution rendering and visualisation of entire flightlines are also allowed. Introduction: The most common approach of interpreting the data, so far, was decomposition of the signal into a sum of Gaussian functions and sequentially extraction of points clouds from the waves (Wanger, Ullrich, Ducic, Malzer , & Studnicka, 2006). Neunschwander et al used this approach for Landover classification (Neuenschwander, Magruder, & Tyler, 2009) while Reightberger et al applied it for distinguishing deciduous trees from coniferous trees (Reitberger, Krzystek, & Stilla, 2006). In 2007, Chauve et al proposed an approach of improving the Gaussian model in order to increase the density of the points cloud extracted from the data and consequently improve point based classifications applied on full-waveform LiDAR data (Chauve, Mallet, Bretar, Durrieu, Deseilligny, & Puech, 2007)

Understanding phenological changes of coniferous forests in Cyprus using time-series of SAR data from 2015 till 2020

1041451473European Regional Development Fun