Synergistic exploitation of geoinformation methods for post-earthquake 3D mapping of Vrisa traditional settlement, Lesvos Island, Greece

The aim of this paper is to present the methodology followed and the results obtained by the synergistic exploitation of geo-information methods towards 3D mapping of the impact of the catastrophic earthquake of June 12th 2017 on the traditional settlement of Vrisa on the island of Lesvos, Greece. A campaign took place for collecting: a) more than 150 ground control points using an RTK system, b) more than 20.000 high-resolution terrestrial and aerial images using cameras and Unmanned Aircraft Systems and c) 140 point clouds by a 3D Terrestrial Laser Scanner. The Structure from Motion method has been applied on the high-resolution terrestrial and aerial photographs, for producing accurate and very detailed 3D models of the damaged buildings of the Vrisa settlement. Additionally, two Orthophoto maps and Digital Surface Models have been created, with a spatial resolution of 5cm and 3cm, respectively. The first orthophoto map has been created just one day after the earthquake, while the second one, a month later. In parallel, 3D laser scanning data have been exploited in order to validate the accuracy of the 3D models and the RTK measurements used for the geo-registration of all the above-mentioned datasets. The significant advantages of the proposed methodology are: a) the coverage of large scale areas; b) the production of 3D models having very high spatial resolution and c) the support of post-earthquake management and reconstruction processes of the Vrisa village, since such 3D information can serve all stakeholders, be it national and/or local organizations

sms: An R Package for the Construction of Microdata for Geographical Analysis

oai:ojs.pkp.sfu.ca:article/687Spatial microsimulation is a methodology aiming to simulate entities such as households, individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the production of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving computational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed

sms

Spatial microsimulation is a methodology aiming to simulate entities such as households, individuals or businesses in the finest possible scale. This process requires the use of individual based microdatasets. The package presented in this work facilitates the production of small area population microdata by combining various datasets such as census data and individual based datasets. This package includes a parallel implementation of random selection with optimization to select a group of individual records that match a macro description. This methodological approach has been used in a number of topics ranging from measuring inequalities in educational attainment (Kavroudakis, Ballas, and Birkin 2012) to estimating poverty at small area levels (Tanton, McNamara, Harding, and Morrison 2007). The development of the method over recent years is driving computational complexity to the edge as it uses modern computational approaches for the combination of data. The R package sms presented in this work uses parallel processing approaches for the efficient production of small area population microdata, which can be subsequently used for geographical analysis. Finally, a complete case study of fitting geographical data with the R package is presented and discussed

Towards an Artificial Intelligence System for Geographical Analysis of Health Data

The complexity of modern scientific research requires advanced approaches to handle and analyse rich and dynamic data. Organizations such as hospitals, hold a great number of health datasets which may consist of many individual records. Artificial Intelligence methodologies incorporate approaches for knowledge retrieval and pattern discovery, which have been proven to be useful for data analysis in various disciplines. Decision trees methods belong to knowledge discovery methodologies and use computational algorithms for the extraction of patterns from data. This work describes the development of an autonomous Decision Support System (“Dth 1.0”) for the real-time analysis of health data with the use of decision trees. The proposed system uses a patient's dataset based on the patients’ symptoms and other relevant information and prepares reports about the importance of the characteristics that determine the number of patients of a specific disease. This work presents the basic concept of decision trees, describes the design of a tree-based system and uses a virtual database to illustrate the classification of patients in a hypothetical intra-hospital case study

Microclimate-Monitoring: Examining the Indoor Environment of Greek Museums and Historical Buildings in the Face of Climate Change

The preservation of cultural artifacts within museums and historical buildings requires control of microclimatic conditions, and the constantly evolving climate certainly poses a challenge to maintaining recommended conditions. Focused on the Archaeological Museum of Delphi and the Church of Acheiropoietos in Greece, our study evaluates the hygrothermal behavior of these buildings with a specific emphasis on the preservation of cultural heritage objects hosted there. An innovative approach to the real-time analysis of data is utilized, aiming to achieve a timely detection of extreme temperature and humidity levels. A one-year monitoring campaign was carried out to achieve a detailed assessment of the indoor climate in selected museums and historical buildings in Greece. The monitoring campaign was performed using dataloggers that were set to measure and record temperature (T) and relative humidity (RH) values hourly. The results allowed for the detection of extreme temperature and relative humidity values, pinpointing the time period that requires more attention. The museum’s heating, ventilation, and air conditioning (HVAC) systems provide temperature control for visitor comfort, but the temperature still rises in summer, highlighting the impact of external climate factors. The church’s lack of HVAC systems widens the temperature range compared to the museum, but significant hourly fluctuations are not observed, underlining the building’s high thermal mass and inertia. Both buildings demonstrate a significant response to changes in outdoor temperature, emphasizing the need for future adaptation to climate change. The HMRhs and PRD indices indicate minimal microclimate risk in both buildings for temperature and RH, reducing the probability of material damage. The church’s slightly higher HMRhs index values, attributed to relative humidity, increases susceptibility due to sensitive materials. Overall, the study highlights the importance of managing microclimatic conditions in historical buildings and proposes careful adaptations for the protection of cultural heritage

A Stakeholders’ Analysis of Eastern Mediterranean Landscapes: Contextualities, Commonalities and Concerns

This study aims at demonstrating and critically assessing high-level landscape stakeholders’ perceptions and understandings of landscape-related issues, threats and problems, in the Eastern Mediterranean, through a purposive comparative research survey of four case studies: Cyprus, Greece, Jordan and Lebanon. Employing qualitative data analysis of intensive stakeholder interviews, performed in the broader context of the MEDSCAPES ENPI-MED project (www.enpi-medscapes.org), the paper draws together the insights and concerns of a total of 61 public entities, private entrepreneurs, academicians and NGO representatives, on landscape knowledge, understanding, management and public awareness, in these four countries. The results point to significant commonalities among them and begin to show relational and synthetic nature of the interrelationship between humans and the landscape, as it developed in the context of the local and regional geographies and histories of this broader region, affected by and involving a series of relevant geophysical, economic, political, social, moral, institutional and other parameters

The Interplay of Objectivity and Subjectivity in Landscape Character Assessment: Qualitative and Quantitative Approaches and Challenges

Landscape character assessment (LCA) methods have been used in the past few decades to analyze, classify, and map landscape types, using objective and subjective approaches, with the aid of both quantitative and qualitative data. This paper addresses and critically evaluates the compromises and ways in which contemporary LCA methodologies employ (or profess they employ) objective versus subjective and quantitative versus qualitative data and analytical tools, in their conceptualization and implementation. It begins with an extensive literature review of the ways in which the objective/subjective and the quantitative/qualitative variables interweave in currently practiced or proposed versions of LCA. With the aid of meta-analysis, the paper traces and discusses the recent evolution, methods, concessions, and risks of such endeavors, and develops an integrative conceptual model for critical assessment, analysis, and negotiation of the interplay between objective-subjective and quantitative-qualitative constituent parts of existing LCA methodologies. It concludes by pointing to pitfalls and prospects, in the broader attempt towards a more concerted, integrative approach to LCA development and practice, both appropriate to its challenges and adaptable to time-space-culture-discipline landscape particularities and means of implementation

Distribution, Population Size, and Habitat Characteristics of the Endangered European Ground Squirrel (Spermophilus citellus, Rodentia, Mammalia) in Its Southernmost Range

The European ground squirrel (Spermophilus citellus) is an endangered species, endemic to Central and Southeastern Europe, inhabiting burrow colonies in grassland and agricultural ecosystems. In recent years, agricultural land-use changes and increased urbanization have largely contributed to a severe population decline across its range, particularly in its southernmost edge. Assessing the population and habitat status of this species is essential for prioritizing appropriate conservation actions. The present study aims to track population size changes and identify habitat characteristics of the species in Greece via a literature search, questionnaires, and fieldwork for assessing trends in population size as well as spatial K-means analysis for estimating its relation to specific habitat attributes. We found that both distribution size (grid number) and colony numbers of the species decreased in the last decades (by 62.4% and 74.6%, respectively). The remaining colonies are isolated and characterized by low density (mean = 7.4 ± 8.6 ind/ha) and low number of animals (mean = 13 ± 16 individuals). Most of the colonies are situated in lowlands and did not relate to specific habitat attributes. Habitat aspect and system productivity (NDVI) were the main factors contributing mostly to the clustering of the existing colonies. These results demonstrate that the species is confined to small, isolated anthropogenic habitats. Specific conservation actions such as population reinforcement, habitat improvement, and specific common agricultural policy measures could effectively improve agroecological zones that are suitable for the maintenance and protection of existing and potential habitats for populations of the species

LSTM-Based Prediction of Mediterranean Vegetation Dynamics Using NDVI Time-Series Data

Vegetation index time-series analysis of multitemporal satellite data is widely used to study vegetation dynamics in the present climate change era. This paper proposes a systematic methodology to predict the Normalized Difference Vegetation Index (NDVI) using time-series data extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS). The key idea is to obtain accurate NDVI predictions by combining the merits of two effective computational intelligence techniques; namely, fuzzy clustering and long short-term memory (LSTM) neural networks under the framework of dynamic time warping (DTW) similarity measure. The study area is the Lesvos Island, located in the Aegean Sea, Greece, which is an insular environment in the Mediterranean coastal region. The algorithmic steps and the main contributions of the current work are described as follows. (1) A data reduction mechanism was applied to obtain a set of representative time series. (2) Since DTW is a similarity measure and not a distance, a multidimensional scaling approach was applied to transform the representative time series into points in a low-dimensional space, thus enabling the use of the Euclidean distance. (3) An efficient optimal fuzzy clustering scheme was implemented to obtain the optimal number of clusters that better described the underline distribution of the low-dimensional points. (4) The center of each cluster was mapped into time series, which were the mean of all representative time series that corresponded to the points belonging to that cluster. (5) Finally, the time series obtained in the last step were further processed in terms of LSTM neural networks. In particular, development and evaluation of the LSTM models was carried out considering a one-year period, i.e., 12 monthly time steps. The results indicate that the method identified unique time-series patterns of NDVI among different CORINE land-use/land-cover (LULC) types. The LSTM networks predicted the NDVI with root mean squared error (RMSE) ranging from 0.017 to 0.079. For the validation year of 2020, the difference between forecasted and actual NDVI was less than 0.1 in most of the study area. This study indicates that the synergy of the optimal fuzzy clustering based on DTW similarity of NDVI time-series data and the use of LSTM networks with clustered data can provide useful results for monitoring vegetation dynamics in fragmented Mediterranean ecosystems

Mesoscale Ocean Feature Identification in the North Aegean Sea with the Use of Sentinel-3 Data

The identification of oceanographic circulation related features is a valuable tool for environmental and fishery management authorities, commercial use and institutional research. Remote sensing techniques are suitable for detection, as in situ measurements are prohibitively costly, spatially sparse and infrequent. Still, these imagery applications require a certain level of technical and theoretical skill making them practically unreachable to the immediate beneficiaries. In this paper a new geospatial web service is proposed for providing daily data on mesoscale oceanic feature identification in the North Aegean Sea, produced by Sentinel-3 SLSTR Sea Surface Temperature (SST) imagery, to end users. The service encompasses an automated process for: raw data acquisition, interpolation, oceanic feature extraction and publishing through a webGIS application. Level-2 SST data are interpolated through a Co-Kriging algorithm, involving information from short term historical data, in order to retain as much information as possible. A modified gradient edge detection methodology is then applied to the interpolated products for the mesoscale feature extraction. The resulting datasets are served according to the Open Geospatial Consortium (OGC) standards and are available for visualization, processing and download though a dedicated web portal