Abstract:We present an accurate digital surface model (DSM) derived from high-resolution Pleiades-1B 0.5 m panchromatic tri-stereo images, covering an area of 400 km 2 over the Athens Metropolitan Area. Remote sensing and photogrammetry tools were applied, resulting in a 1 m × 1 m posting DSM over the study area. The accuracy of the produced DSM was evaluated against measured elevations by a differential Global Positioning System (d-GPS) and a reference DSM provided by the National Cadaster and Mapping Agency S.A. Different combinations of stereo and tri-stereo images were used and tested on the quality of the produced DSM. Results revealed that the DSM produced by the tri-stereo analysis has a root mean square error (RMSE) of 1.17 m in elevation, which lies within the best reported in the literature. On the other hand, DSMs derived by standard analysis of stereo-pairs from the same sensor were found to perform worse. Line profile data showed similar patterns between the reference and produced DSM. Pleiades tri-stereo high-quality DSM products have the necessary accuracy to support applications in the domains of urban planning, including climate change mitigation and adaptation, hydrological modelling, and natural hazards, being an important input for simulation models and morphological analysis at local scales.

Nowadays, coastal areas are exposed to multiple hazards of increasing severity, such as coastal floods, erosion, subsidence due to a combination of natural and anthropogenic factors, including climate change and urbanisation. In order to cope with these challenges, new remote sensing monitoring solutions are required that are based on knowledge extraction and state of the art machine learning solutions that provide insights into the related physical mechanisms and allow the creation of innovative Decision Support Tools for managing authorities. In this paper, a novel user-friendly monitoring system is presented, based on state-of-the-art remote sensing and machine learning approaches. It uses processes for collecting and analysing data from various heterogeneous sources (satellite, in-situ, and other auxiliary data) for monitoring land cover and land use changes, coastline changes soil erosion, land deformations, and sea/ground water level. A rule-based Decision Support System (DSS) will be developed to evaluate changes over time and create alerts when needed. Finally, a WebGIS interface allows end-users to access and visualize information from the system. Experimental results deriving from various datasets are provided to assess the performance of the proposed system, which is implemented within the EPIPELAGIC bilateral Greece-China project. The system is currently being installed in the Greek case study area, namely Thermaikos Gulf in Thessaloniki, Greece.

<p>Urban green infrastructure in the form of green roofs and vertical gardens is gradually becoming a mainstream development option to mitigate the negative impacts of dense urbanization, and primarily those associated with the urban heat island effect and the consequent vulnerability due to climate change (Nektarios and Ntoulas, 2017). Nevertheless, the quantification of the effect of green infrastructure in comparison to conventional infrastructure as well as tree parks and gardens, can be a challenge in a rapidly changing urban environment, especially due to historical gaps in environmental parameter monitoring. Here we propose the use of land surface temperature (LST) [<sup>o</sup>C] produced using freely available LandSat imagery at 30 m resolution, to evaluate the effect of green infrastructure on urban surface temperature. The method relies on the comparison of historical LST timeseries of an area of interest which has undergone urban greening interventions with adjacent city blocks that have retained their conventional urban character. The method is applied to evaluate the impact of the recently constructed Eco Campus Orange (ECO) garden, which has resulted from the renovation of 4 city blocks in Paris, France. Within an area over 3 ha, ECO employs environmentally friendly materials and 100,000 plants to feature 2,300 m<sup>2</sup> of green wall and “the largest green roof of Europe”. For the area of interest, over 250 LandSat 5, 7, and 8 multispectral images dating from 2010 to 2020, were analyzed after Ermida et al. (2020). Results show that, since its construction, LST at ECO quickly dropped by over 2 <sup>o</sup>C, reaching the LST levels of adjacent urban parks. The method is ideal for ambient temperature timeseries reconstruction where long-term monitoring is sparce and can be applied to evaluate drastic landscape changes such as urban greening or vegetation thinning.</p><p><strong>References</strong></p><p>Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.M., Trigo, I.F., 2020. Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sens. https://doi.org/10.3390/RS12091471</p><p>Nektarios, P.A., Ntoulas, N., 2017. Designing green roofs for arid and semi-arid climates. The route towards the adaptive approach, in: Acta Horticulturae. International Society for Horticultural Science, pp. 197–202. https://doi.org/10.17660/ActaHortic.2017.1189.39</p><p><strong>Acknowledgements</strong></p><p>The research was co-financed by the European Union and Greek national funds through the Operational Program RIS3Crete (COMPOLIVE: ΚΡΗΡ3-0028773)</p><p>The research of MG was co-financed by the European Union and Greek national funds through the Operational Program "Human Resource Development, Education and Lifelong Learning", under the Act "STRENGTHENING post-doctoral fellows / researchers - B cycle" (MIS 5033021) implemented by the State Scholarship Foundation.</p>

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A very high-resolution DSM covering an area of 400km 2 over the Athens Metropolitan 10Area has been produced using Pleiades 1B 0,5m panchromatic tri-stereo images. Applied Remote 11Sensing and Photogrammetry tools have been used resulted in a 1x1m DSM over the study area. 12DSM accuracy has been evaluated by comparison with measured elevations with D-GPS and a