Prof. Petros G. Patias Profile

Prof. Petros G. Patias

at Aristotle Univ of Thessaloniki

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Publications (8)

Proceedings Article | 13 September 2024 Open Access

Advancing forest biodiversity conservation with the EL-BIOS digital twin: an integration of LiDAR and multispectral earth observation data

I. Karolos, K. Bellos, V. Alexandridisale, I. Chrysafis, H. Georgiadis, C. Pikridas, V. Tsioukas, P. Patias, G. Mallinis

Proceedings Volume 13212, 1321214 (2024) https://doi.org/10.1117/12.3037300

KEYWORDS: Point clouds, Biodiversity, 3D applications, LIDAR, Unmanned aerial vehicles, Data processing, RGB color model, Georeferencing, Receivers, Satellite navigation systems

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Proceedings Article | 21 September 2023 Paper

Development of a smart tool for the monitoring urban biodiversity using earth observation data, in-situ sensors, and citizen science in the city of Thessaloniki

Charalampos Georgiadis, Georgios Mallinis, Petros Patias

Proceedings Volume 12786, 127860H (2023) https://doi.org/10.1117/12.2680537

KEYWORDS: Biodiversity, Data acquisition, Earth observing sensors, Ecosystems, Visualization, Sensors, Satellites, Image classification, Algorithm development, Vegetation

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Proceedings Article | 27 June 2019 Paper

Demonstrating the potential of remote sensing to support sustainable development goals implementation: case studies over Greece

M. Tassopoulou, N. Verde, G. Mallinis, H. Georgiadis, D. Kaimaris, P. Patias

Proceedings Volume 11174, 111740D (2019) https://doi.org/10.1117/12.2533634

KEYWORDS: Ecosystems, Earth observing sensors, Remote sensing, Satellites, Water, Satellite imaging, Landsat, Image processing, Data acquisition, Image classification

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Proceedings Article | 27 June 2019 Paper

Essential variables: describing the context, progress, and opportunities for the remote sensing community

Petros Patias, Natalia Verde, Maria Tassopoulou, Charalampos Georgiadis, Dimitris Kaimaris

Proceedings Volume 11174, 111740C (2019) https://doi.org/10.1117/12.2533604

KEYWORDS: Climatology, Remote sensing, Ecosystems, Geodesy, Water, Environmental sensing, Agriculture, Renewable energy, Satellites, Data processing

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The concept of Essential Variables (EVs) has emerged within the remote sensing community in recent years. The EVs, having previously defined as a minimal set of variables that determines the system’s state and development, have attracted considerable interest not only for remote sensing scientists, but from several, diverse thematic groups and communities. The driving forces behind this evolution, relates primary to the need to support national to global monitoring, reporting, research, and forecasting of complex earth systems, to the necessity for an essential set of parameters that could be used for monitoring progress towards the goals of different thematic communities as well as to the requirement to support consistent, objective temporal information provision for policy development and implementation. Also, considering the availability of sensor data with similar characteristics from different satellites orbiting around earth, there is a need to standardize the information extracted, independently of the observational platform and the processing algorithms as well as to provide this information in a more streamlined, comprehensible form to end-users not well acquainted with the remote sensing technology and terminology. So far, EVs have been introduced and adopted for monitoring oceans, climate and biodiversity systems as well as measuring progress towards UN Sustainable Development Goals (SDGs) implementation, while several other communities are in the process of adopting this concept in their domain. This study will review the recent progress as well as the current challenges and future developments in the research agenda around EVs. In particular we review the current state of EVs and their connection to the different scientific communities, as well as cross-domain interactions and synergies. We then describe potential new thematic areas and scientific communities where the concept of EVs could be applied and provide the outline of the process to be followed for identifying these variables.

Proceedings Article | 6 August 2018 Paper

Coastal 3D mapping using very high resolution satellite images and UAV imagery: new insights from the SAVEMEDCOASTS project

Petros Patias, Charalampos Georgiadis, Marco Anzidei, Dimitrios Kaimaris, Christos Pikridas, Giorgios Mallinis, Fawzi Doumaz, Alessandro Bosman, Vincenzo Sepe, Antonio Vecchio

Proceedings Volume 10773, 107730V (2018) https://doi.org/10.1117/12.2325540

KEYWORDS: Satellites, Unmanned aerial vehicles, Image processing, Satellite imaging, Spatial resolution, Earth observing sensors, Image resolution, Clouds, Data processing, Orthophoto maps

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Global climate changes are a main factor of risk for infrastructures and people living along the coasts around the world. In this context, sea level rise, coastal retreat and storm surges pose serious threats to coastal zones. In order to assess the expected coastal changes for the next decades, a detailed knowledge of the site’s topography (coastline position, DTM, bathymetry) is needed. This paper focuses on the use of very high resolution satellite data and UAV imagery for the generation of accurate very-high and ultra-high mapping of coastal areas. In addition, the use of very high resolution multi-spectral satellite data is investigated for the generation of coastal bathymetry maps. The paper presents a study for the island of Lipari and the coasts of Cinque Terre (Italy) and the island of Lefkas (Greece). For Lefkas, two areas of the island were mapped (the city of Lefkas and its adjoining lagoon in the north side of the island, and the Bay of Vasiliki at the south part of the island) using World View 1, and Wolrd View 3 satellite images, and UAV imagery. The satellite processing provided results that demonstrated an accuracy of approximately 0.25 m plannimetrically and 0.70 m vertically. The processing of the UAV imagery resulted in the generation of DTMs and orthophotos with an accuracy of approximately 0.03-0.04 meters. In addition, for the Vasiliki bay in the south of the island the World View 3 imagery was used for the estimation of a bathymetry map of the bay. The achieved results yielded an accuracy of 0.4 m. For the sites of Lipari and Cinque Terre (both in Italy), UAV surveys allowed to extract a DTM at about 2 cm of pixel resolution. The integration of topographic data with high resolution multibeam bathymetry and expected sea level rise from IPCC AR5 2.6 and 8.5 climatic scenarios, will be used to map sea level rise scenarios for 2050 and 2100, taking into account the Vertical Land Motion (VLM) as estimated from CGPS data. The above-mentioned study was realized during the implementation of the SAVEMEDCOASTS project (Sea level rise scenarios along the Mediterranean coasts, funded by the European Commission ECHO A.5, GA ECHO/SUB/2016/742473/PREV16, www.savemedcoasts.eu).

Proceedings Article | 6 August 2018 Paper

Assessment and mapping of urban environmental quality using remote sensing and geospatial data

Ifanti Danai, Maria-Strati Tsakiri, Giorgos Mallinis, Harris Georgiadis, Dimitris Kaimaris, Petros Patias

Proceedings Volume 10773, 107731U (2018) https://doi.org/10.1117/12.2325512

KEYWORDS: Pollution, Vegetation, Remote sensing, Roads, Environmental sensing, Geographic information systems, Earth observing sensors, Landsat

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Proceedings Article | 6 September 2017 Paper

Evaluating horizontal positional accuracy of low-cost UAV orthomosaics over forest terrain using ground control points extracted from different sources

Petros Patias, Fotis Giagkas, Charalampos Georgiadis, Giorgos Mallinis, Dimitris Kaimaris, Vassileios Tsioukas

Proceedings Volume 10444, 104440U (2017) https://doi.org/10.1117/12.2278008

KEYWORDS: Unmanned aerial vehicles, Forestry

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Within the field of forestry, forest road mapping and inventory plays an important role in management activities related to wood harvesting industry, sentiment and water run-off modelling, biodiversity distribution and ecological connectivity, recreation activities, future planning of forest road networks and wildfire protection and fire-fighting. Especially in countries of the Mediterranean Rim, knowledge at regional and national scales regarding the distribution and the characteristics of rural and forest road network is essential in order to ensure an effective emergency management and rapid response of the fire-fighting mechanism. Yet, the absence of accurate and updated geodatabases and the drawbacks related to the use of traditional cartographic methods arising from the forest environment settings, and the cost and efforts needed, as thousands of meters need to be surveyed per site, trigger the need for new data sources and innovative mapping approaches. Monitoring the condition of unpaved forest roads with unmanned aerial vehicle technology is an attractive option for substituting objective, laboursome surveys. Although photogrammetric processing of UAV imagery can achieve accuracy of 1-2 centimeters and dense point clouds, the process is commonly based on the establishment of control points. In the case of forest road networks, which are linear features, there is a need for a great number of control points. Our aim is to evaluate low-cost UAV orthoimages generated over forest areas with GCP’s captured from existing national scale aerial orthoimagery, satellite imagery available through a web mapping service (WMS), field surveys using Mobile Mapping System and GNSS receiver. We also explored the direct georeferencing potential through the GNSS onboard the low cost UAV. The results suggest that the GNSS approach proved to most accurate, while the positional accuracy derived using the WMS and the aerial orthoimagery datasets deemed satisfactory for the specific task at hand. The direct georeferencing procedure seems to be insufficient unless an onboard GNSS with improved specifications or Real-Time Kinematic (RTK) capabilities is used.

Proceedings Article | 17 August 1994 Paper

Relative orientation of image triples using straight linear features

Elli Petsa, Petros Patias

Proceedings Volume 2357, (1994) https://doi.org/10.1117/12.182907

KEYWORDS: Photogrammetry, Machine vision, Computer vision technology, Cameras, Reconstruction algorithms, Mathematical modeling, Systems modeling, Calibration, Computer simulations, Digital photography

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