During the lockdown of the COVID-19 pandemic, most countries imposed mobility restrictions such as physical distancing from others, “self-isolation”, and/or “quarantine”. Although these “isolation” measures had been effective in hindering the spread of the virus effectively, people’s mental health can be strongly affected by these “isolation” measures. Some researchers found that the mental health problem indeed increased during the lockdown. In order to reduce the negative effects of isolation on mental health, some studies suggested that people should be brought closer to nature spaces during the lockdown. In this regard, policymakers are paying more attention to Nature-based Solutions (NBS) (e.g. green roofs, gardens, and urban parks), which can potentially improve people's physical and mental health via interactions between people and nature.

In order to enhance the connectivity of the landscape to improve the ecosystem services and reduce health risks with the help of NBS in a post-COVID world, it is significant to consider the heterogeneity of the spatial distribution of green spaces. A number of studies have found that estimates of green space areas are scale-dependent, it is therefore important to investigate the intrinsic complexity of the heterogeneity of the green spaces across a range of scales. This could be achieved with the help of the universal multifractal (UM) framework (Schertzer and Lovejoy, 1987), a stochastic approach widely used to quantify the variability of geophysical fields across a range of scales. This study aims to improve the landscape connectivity of the green spaces in Paris across scales with the help of the UM cascade model.

To achieve the aim of this study, we first quantified the heterogeneous spatial distributions of green spaces of the selected areas by using the fractal dimension. Then, a distance analysis is performed for non-green spaces to green spaces, and a series of NBS scenarios are created based on integrating potential NBS into the current landscape by using the UM cascade model. Finally, the spatial distributions of the NBS combined with the original green spaces are quantified by the fractal dimension and distance analysis. The results indicate that NBS can effectively improve the connectivity of the landscape and has the potential to reduce the physical and mental risks caused by COVID-19. More specifically, this study proposes a scale-independent approach for enhancing the multiscale connectivity of the NBS network in urban areas and provides quantitative suggestions for cities in a post-COVID world.

How to cite: Qiu, Y., Tchiguirinskaia, I., and Schertzer, D.: Enhancing landscape connectivity by Nature-based Solutions for cities in a post-COVID world: a case study in Paris, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-820, https://doi.org/10.5194/egusphere-egu23-820, 2023.

Expansion of built-up areas has consumed large areas of natural ecological patches in many cities around the world, affecting environmental and living quality of urban residents.  Managing urban landscape and urban trees has gained a special attention in Vietnam in recent years. The green space development plan for Hanoi to 2030 and a vision to 2050 has targeted to reach 62% of green spaces. However, there is lack of detailed green space development plan at the district and commune/ward levels. This study aims to assess urban green space area and quality in Hanoi at the commune/ward levels using remote sensing and population data. The study uses combined remote sensing data from Google Earth, Sentinel-2, and Normalized Difference Vegetation Index (NDVI) to analyze urban green quality and space for Hanoi. The urban green space will be combined with population data at commune/ward level to estimate urban tree cover per person. The research results can contribute to improve the credibility and scientifically of green space construction so that urban planning can adapt and serve the city and its residents and achieve green development.

How to cite: Reimuth, A., Nong, D. H., and Ngo, S. T.: Assessing urban green space area and quality using remote sensing and population data: A case study of Hanoi urban districts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14511, https://doi.org/10.5194/egusphere-egu23-14511, 2023.

The cooling impact of green roofs is highlighted in the context of urbanisation and urban heat island (UHI) effect. And it is usually described and quantified by evapotranspiration (ET) processes. Understanding ET process is the key to optimize cooling effect. ET estimation can be achieved either directly (weighing lysimeters) or indirectly (e.g., Penman-Monteith equation). Micro-meteorological approaches have been developed in recent years. Among which scintillometer can evaluate ET by its measurement parameter C_n² (which corresponds to the fluctuations of air refractive index n ) in combination with surface energy balance (SEB) and Monin-Obukhov similarity theory (MOST) . Hence, C_n² improvement in Cn2 data would result in better ET estimation. But it is often overlooked and very little research has focused on it. In this project, the research area lies on the top of the Carnot and Bienvenüe buildings in Ecole des Ponts Paristech. Covering an area of ​​1 ha, it is a wavy and vegetated large green roof, known as the Blue Green Wave (BGW). Data from a large aperture scintillometer (LAS) with 10-minute time step during December 2019 and January 2020 on BGW is used in this study. Three estimates of C_n²(Cn2_UCn2, Cn2_PUCn2 and Cn2_Var) were analysed with structure function and universal multifractal model (UM). Such framework has been widely use to characterize geophysical fields extremely variable across wide range of space-time scales. There are two relevant parameters in an UM model, the mean codimension of intermittency C₁≥0and multifractality index 0≤α≤2. α=0, indicates monofractal; α=2, indicates log-normal model. Data in UM framework is analysed by Trace Moment (TM) method and Double Trace Moment (DTM) method. All of estimates demonstrated scale invariance, which could be used for upscaling and downscaling. Cn2_Var performed well even during measurement malfunction, but UM analysis showed it was contradictory to the hypothesis of lognormality. It implies the way it calculates Cn2_Var need some revisions and an assessment of the scintillometer could be achieved by analysing C_n². This research provides a complete grasp of the properties of C_n² and sets the stage for its future application in precise ET estimates.

How to cite: Zhu, S., Gires, A., Maksimovic, C., Tchiguirinskaia, I., and Schertzer, D.: Multifractal analysis of Cn2 scitillometer data and consequences for evapotranspiration estimates in urban areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14618, https://doi.org/10.5194/egusphere-egu23-14618, 2023.

The improved ability of imaging sensors to capture very high resolution (VHR) remote sensing images has been boosted by recent enhancement in the data processing algorithms. This improvement raises the potential of providing precise scene understanding for many applications. For instance, the task of semantic segmentation is an important one in the context of 3D building modelling. In this work, the main objective is to show the potential of using images of a stereo pair as inputs to a neural network trained for semantic segmentation into different urban classes. Actually, instead of increasing artificially the amount of training data, the use of stereo pair images can be seen as a realistic data augmentation, where the model will be trained to see the same object from different acquisition angles. From an experimental point of view, the results show that the method achieves better performances and gives a greater ability to generalize compared to the use of a single view. 

The segmentation process is performed using an encoder-decoder network architecture, namely the U-net network which includes an EfficientNet for the encoder part and a RefineNet for the decoder stage. The model is trained on Pleiades images involving different sources of ground truth (OpenStreetMap, IGN databases and in-house LCLU AI4GEO hierarchical labelled data). Additionally to the spectral information, height information is also considered to enhance the segmentation accuracy. This latter information is obtained using digital surface model (DSM). Indeed, classes identifying urban areas (building class for example) can be more easily discerned according to their height information. 

Furthermore, since Pleiades images are used as inputs of the proposed model, some geometrical issues need to be handled. To remove this complexity, a simulated imaging geometry of a perfect instrument is designed with no optical distortion and no high attitude perturbations. Resulting geometry is commonly called perfect sensor geometry. Since then, to avoid problems of geometric offsets between different data sources (satellite images in perfect geometry), terrain geometry of DSM/DTM and various ground truth databases, several tools have been developed to allow conversion between different geometries. The ortho-rectification is a commonly used geometrical correction that aims at presenting images as if they had been captured from the vertical. Therefore, this correction requires the availability of a HR digital terrain model (DTM) and may result on some distortions. In particular, some area may be occluded and others may arise a spreading effect of buildings.

 To address this issue, and preserve the native image information of the perfect sensor geometry, one key contribution of this work is to map the DSM data and ground truth image into the perfect sensor geometry. By doing this geometric processing and object positioning during the training process, better overlays between different data sources (stereo pair images, DSM model and ground truth data) are ensured and geometrical distortions and offsets can be avoided. In addition, the inferences can be done directly on the perfect sensor geometry without having to go through terrain geometries which requires high resolved DSM/DTM models.

How to cite: Akodad, S. and Lassalle, P.: Automatic Land Cover Segmentation from Perfect Sensor Stereo Images with Height Information, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9983, https://doi.org/10.5194/egusphere-egu23-9983, 2023.

The Einstein Telescope (ET) is a proposed underground infrastructure to host a third-generation, gravitational-wave observatory. There are currently two candidate sites to host it: one of this is located in Sardinia region (Italy), in a favourable geological context, the other one in the Meuse-Rhine Euregion. Site-characterization studies are under way towards the site selection, which is expected for 2024. The scope work of this research is to evaluate the surface deformation of this site by integration of remote sensing techniques with geological and geophysical data. In this framework the PSI (Persistent Scattered Interferometry) technique with SAR data is the proposed approach for the analysis of a long time-series imagery. Although recent crustal movements in the study area are supposed to be very small (≃ - 0.5 mm/years from 2014 as measured by EUREF Permanent Network https://epnd.sgo-penc.hu), ESA Sentinel-1 data from Copernicus program, represents an effective tool to update this knowledge and monitor the phenomenon. A first analysis has been performed in the study area using the Snap2Stamps methodology. During the first assessment of this research, this methodology has been tested to a dataset of 94 images from Sentinel-1. The radar data (SLC, Single Looking complex) acquired from January 2021 to July 2022 for both descending and ascending orbits on an area of 250 sq km has been managed. The applied methodology requires a long-time for the processing in order to derive vertical velocities, and we considered the opportunity to use a cloud service. So, we exploited the possibility offered by SNAPPING service provided by Terradue (https://www.terradue.com/portal/), a cloud on-demand computing service for Sentinel-1 Multi-Temporal DInSAR processing, based on integrated SNAP and StaMPS chain. In this service, the dataset can be improved, considering a longer time of acquisition, exploiting the complete Sentinel revisiting time, starting from 2014.

The first results of this analysis have been calibrated with the existing GNSS measures provided by EUREF using the data of the Nuoro station. The ground vertical displacement calculations, composing data from both acquisition orbits, confirm the existing evaluations and extend the current information to the whole study area. Moreover, it will be possible to consider also future acquisition with a continuous monitoring process.

These results can be considered an important value for the proposed Italian site and the ET infrastructure realization.

How to cite: Dessì, F., Melis, M. T., Da Pelo, S., and Funedda, A.: Monitoring land deformation through PSI technique for Einstein Telescope site characterization of Sos Enattos (Sardinia, Italy), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14807, https://doi.org/10.5194/egusphere-egu23-14807, 2023.

Malaria is a widespread, mosquito-borne, potentially lethal infectious disease that affects humans and other animals. Its prevention and treatment have been targeted in science and medicine for hundreds of years. During the 20th century it was widespread in the Middle East, including Cyprus, and was one of the most important health hazards worldwide. In 1967, the World Health Organisation (WHO) declared Cyprus as malaria-free, an impressive feat considering malaria had plagued the island since the Roman period (Demetrios, 2009). We conducted a critical analysis of anti-malarial campaigns on urban and rural districts in Cyprus, under British colonial rule (1878 – 1960), in the context of malarial disease knowledge in health surveillance and care policy. Under the HIGH-PASM (High-resolution palaeoclimate records and social vulnerability for the last Millennium) project, we present a methodology for constructing database tools relative to heterogeneously distributed historical sources. The aim of this research is to study the impact of the anti-malarial works on the Cypriot landscape as the social-political situation and the methods implemented did not follow the stringent protocols that exist today. Main issues are the complexity regarding British, Ottoman and French social and political roles, ground truth data extracted from historical sources - that need critical analysis, and the complex phenomena under scope. Primary sources are annual medical reports, written by British medical officers, published from 1913 to 1953. The main focus of these actions linked geography to healthcare issues by eliminating the newly identified malaria-vector by directly influencing the mosquito’s habitat, thus indirectly affecting the Cyprus landscape. The assertion, verification and evaluation of the before mentioned actions requires the medical reports to be contextually placed alongside secondary sources (for example correspondences, journal articles, conference proceedings, etc), which were produced or disseminated during this time period by different actors or groups of actors. We aim to apply methodologies used in digital humanities and conceptual modelling within geosciences to verify and understand spatial-temporal information that may be found within archival references. Raw data are extracted from a small corpus to produce meta-data (data sense ((Hui, 2015)) using existing cultural heritage vocabularies (CIDOC CRM base and extensions) relative to different fields and objects to model spatial-temporal events and align these data with authority databases using W3C (World Wide Web Consortium) semantic web standards (technologies). Given the British colonial role in the governance of the island, there is a lack of empirical evidence on the choices of techniques or actions employed. Thus, this meta-data conceptual modelling and raw data collection, as a data management approach, offers a syntactic, semantic and pragmatic understanding of archival sources. This methodology ultimately aims to study the impact on the landscape of the anti-malarial campaigns by bridging gaps in existing literature by the digitisation of physical reports and digitalisation of a healthcare system from the late 19^th to 20^th century.

How to cite: Danielkutty, L. E., Krummeich, R., Couillet, A., Charalambidou, I., and Eliot, E.: Constructing an event centred modelling process to produce spatial and temporal data for the critical study of the impact of British colonial rule (1878 – 1960) on the Cypriot landscape through their anti-malarial campaigns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11346, https://doi.org/10.5194/egusphere-egu23-11346, 2023.

The remediation of brownfield is vital to sustainable place-making and levelling up across the country. It provides an improved local environment that can unlock regeneration and the social, economic and ecological revitalisation of communities. However the total benefits of remediation are not fully understood or utilised in decision making. As a result, sites can remain derelict for years and opportunities to optimise value from public and private investment are missed.

Jacobs and BGS undertook research for the Environment Agency in England to evaluate the feasibility of developing a tool, which included:

• A virtual workshop using MURAL to enable digital interaction and collaboration to refine scope, define data requirements and map project stakeholders;
• Primary benefit and user requirements research, including looking at the potential impact of a tool through the development of a Theory of Change model and focussed interviews with key stakeholders to understand user requirements.
• Review of academic and grey literature;
• Accelerated design sprint to frame the problem/opportunity, explore technology agonistic solutions for the tool and develop into a storyboard.
• Develop a low fidelity prototype as a blueprint of how a tool might look.

The outcome of the work indicated there is both a need and demand for such a tool. It was also demonstrated to be technically feasible through the literature review and design sprint. Such a tool would have an extremely positive impact on the perceptions of brownfield, shifting it from a constraint to an opportunity. The presentation will provide a summary of the methods, an overview of the results and a demonstration of a prototype digital tool. Our disucssion will focus on the opportunities presented by using systems thinking combined with design thinking to influence the approach taken to planning and redeveloping brownfield sites.  

How to cite: Beriro, D., Macklin, Y., Thrasher, J., Griggs, D., and Haslam, A.: A feasibility study for a novel remediation and sustainable growth digital tool for the Environment Agency, England, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11851, https://doi.org/10.5194/egusphere-egu23-11851, 2023.

A significant number of papers focusing on the relationships between COVID-19 diffusion and geographic factors is available in literature. The same applies to the use of geographic techniques (e.g., spatial tools and mapping) for the study of the pandemic. Although the literature on these topics is already abundant, a detailed and comprehensive review is still lacking.
In this context, the purpose of this paper is to fill the existing gap by presenting a literature review of geographical studies dealing with the COVID-19 pandemic. The review is aimed at: i) understanding the role of geographic/territorial determinants (e.g., geographic location of confirmed cases, climatic and environmental characteristics, urbanization) in the spread of COVID-19; ii) identifying common approaches, materials, and methods used in the study of the COVID-19 outbreak from a geographical perspective; iii) recognising possible research gaps to address future in-depth analyses.
To achieve these goals a literature review was made concerning the application of geographical approaches for the study of one or more geographical factors/variables, as well as socioeconomic factors in relation to the outbreak and diffusion of the COVID-19 pandemic. The main academic literature databases were inquired. More than 80 papers were reviewed and categorized according to different criteria, e.g., considered variables, investigated period, spatial and temporal resolution and applied methodologies.
This research is part of an interdisciplinary project (“DISCOV19”) funded by the University of Modena and Reggio Emilia and aiming at identifying the main vulnerability and risk factors related to COVID-19 outbreak and at formulating prevention and management schemes with a focus on the Province of Modena (Northern Italy). The investigation crosses different disciplines: i) public health epidemiology, investigating the contagion modalities and health and socio-demographic predisposing factors; ii) economic-statistical methodology, pointing out the structural characteristics of the networks that convey the contagion and the main social, technological and management vulnerabilities with respect to COVID-19 spread; iii) geography and geomorphology, for thematic mapping and spatial analysis of COVID-19 outbreak and understanding the role of environmental and physical-geographical factors on COVID-19 incidence. The review here presented fits into this context being one of the first outputs of the project implementation.

How to cite: Coratza, P., Ghinoi, A., Palandri, L., Righi, E., Rizzi, C., Soldati, M., and Vandelli, V.: The influence of geographical factors on COVID-19 outbreak: A literature review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8235, https://doi.org/10.5194/egusphere-egu23-8235, 2023.